Learning device, learning method, identification device, identification method, and program

ABSTRACT

A learning device includes a feature-point extracting section extracting feature points from a generation image, a feature-point feature-quantity extracting section extracting feature-point feature-quantities representing features of the feature points, a total-feature-quantity generating section generating a total feature quantity represented by a multi-dimensional vector, and an identifier generating section generating an identifier using the total feature quantity and a true label indicating whether or not the generation image is a positive image or a negative image.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a learning device, a learning method,an identification device, an identification method, and a program, andmore particularly to a learning device, a learning method, anidentification device, an identification method, and a program which canimprove both discrimination and invariance in the identification ofwhether or not a subject viewed in an image is a predeterminedidentification object.

2. Description of the Related Art

A method of performing matching using a template where identificationobjects are broadly described exists as a method of identifying anobject as an identification object located within an image from theimage captured by a camera.

That is, the identification method in the related art prepares atemplate where identification objects are broadly described, that is, atemplate of textures of all identification objects, and matches an imageof an object to be identified (an object to be processed) with thetemplate.

However, it is difficult to process a hidden or distorted part of theidentification object viewed in an image as an object to be processed ina matching process using the template where identification objects arebroadly described.

There is a method of observing a local area of an image to be processed,extracting feature quantities from each local area, and performing anidentification process by employing a combination of the featurequantities of the local area (a set of the feature quantities of thelocal area), that is, a vector using the feature quantities of eachlocal area as components.

When a set of feature quantities of a local area is used, ahigh-precision identification process may be performed by partiallysolving the problem of a hidden or distorted part of an identificationobject which is difficult to be processed in the method using a templatewhere identification objects are broadly described.

A feature quantity of a local area is used for object categoryidentification as well as individual object identification. For example,a method of identifying a specific category such as the face of a personor the like using a feature quantity of a local area has been proposed(for example, see P. Viola and M. Jones, “Robust Real-time FaceDetection”, cvpr 2001).

Various frameworks for category identification have been proposed. Forexample, there is a framework using a histogram of BoF (Bag of Features)(for example, see G. Csurka, C. Bray, C. Dance, and L. Fan, “VisualCategorization with Bags of Keypoints”, ECCV 2004), a framework using acorrelation of feature quantities (for example, see Japanese UnexaminedPatent Application Publication No. 2007-128195), or the like as aframework proposed for the category identification.

For example, an SIFT feature quantity (for example, see D. Lowe, “ObjectRecognition from Local Scale-Invariant Features”, ICCV 1999) or anoutput (response) of a steerable filter (for example, see J. J. Yokonoand T. Poggio, “Oriented Filters for Object Recognition: an empiricalstudy”, FG 2004) have been proposed as a feature quantity of a localarea for use in identification.

SUMMARY OF THE INVENTION

The discrimination for identifying (discriminating) an identificationobject and others and the invariance for identifying the motion of anobject to be identified even when the identification object is rotatedor distorted are necessary for identifying an individual object or anobject category.

However, the discrimination and invariance are generally in a trade-offrelationship. Therefore, it is difficult to improve both thediscrimination and the invariance even when an SIFT feature quantity ora response of a steerable filter is used as a feature quantity for theidentification of whether or not a subject viewed in an image is apredetermined identification object such as the identification of anindividual object or an object category.

It is desirable to improve both discrimination and invariance in theidentification of whether or not a subject viewed in an image is apredetermined identification object.

According to a first embodiment of the present invention, a learningdevice, or a program for making a computer function as a learningdevice, includes feature-point extracting means for extracting featurepoints as characteristic points from one of a plurality of generationimages including a positive image in which a predeterminedidentification object is viewed and a negative image in which noidentification object is viewed for use in learning to generate anidentifier for identifying whether or not a subject viewed in an imageis the identification object, feature-point feature-quantity extractingmeans for extracting feature-point feature-quantities representingfeatures of the feature points of the generation image,total-feature-quantity generating means for generating a total featurequantity represented by a multi-dimensional vector from thefeature-point feature-quantities of the generation image, wherein thetotal feature quantity represents a feature of the entire generationimage, and identifier generating means for generating the identifierusing the total feature quantity of the generation image and a truelabel indicating whether or not the generation image is a positive imageor a negative image, wherein the feature-point feature-quantityextracting means divides a feature-point area into a plurality of smallareas by separating the feature-point area as an area having a featurepoint as a center in an angular direction and a distance direction onthe basis of the feature point in each of a plurality of response imagesobtained by filtering the generation image by a plurality of filtershaving different characteristics, produces a statistical quantity ofpixel values of a small area for each of the plurality of small areas,and sets the statistical quantity of each of the plurality of smallareas obtained from each of the plurality of response images for thefeature point as a feature-point feature-quantity of the feature point,and wherein the identifier generating means generates the identifier forperforming identification using a dimensional feature quantity todecrease an error value representing an identification error level ofthe positive and negative images among a plurality of dimensionalfeature quantities which are components of the multi-dimensional vectoras the total feature quantity, and generates dimensional informationrepresenting a dimension of the dimensional feature quantity to decreasethe error value.

According to the first embodiment of the present invention, a learningmethod includes the steps of extracting feature points as characteristicpoints from one of a plurality of generation images including a positiveimage in which a predetermined identification object is viewed and anegative image in which no identification object is viewed for use inlearning to generate an identifier for identifying whether or not asubject viewed in an image is the identification object, extractingfeature-point feature-quantities representing features of the featurepoints of the generation image, generating a total feature quantityrepresented by a multi-dimensional vector from the feature-pointfeature-quantities of the generation image, wherein the total featurequantity represents a feature of the entire generation image, andgenerating the identifier using the total feature quantity of thegeneration image and a true label indicating whether or not thegeneration image is a positive image or a negative image, wherein theextracting step is performed by dividing a feature-point area into aplurality of small areas by separating the feature-point area as an areahaving a feature point as a center in an angular direction and adistance direction on the basis of the feature point in each of aplurality of response images obtained by filtering the generation imageby a plurality of filters having different characteristics, producing astatistical quantity of pixel values of a small area for each of theplurality of small areas, and setting the statistical quantity of eachof the plurality of small areas obtained from each of the plurality ofresponse images for the feature point as a feature-pointfeature-quantity of the feature point, and wherein the generating stepis performed by generating the identifier for performing identificationusing a dimensional feature quantity to decrease an error valuerepresenting an identification error level of the positive and negativeimages among a plurality of dimensional feature quantities which arecomponents of the multi-dimensional vector as the total featurequantity, and generating dimensional information representing adimension of the dimensional feature quantity to decrease the errorvalue.

In the first embodiment of the present invention, feature points ascharacteristic points are extracted from one of a plurality ofgeneration images including a positive image in which a predeterminedidentification object is viewed and a negative image in which noidentification object is viewed for use in learning to generate anidentifier for identifying whether or not a subject viewed in an imageis the identification object. Feature-point feature-quantitiesrepresenting features of the feature points of the generation image areextracted. A total feature quantity represented by a multi-dimensionalvector is generated from the feature-point feature-quantities of thegeneration image, wherein the total feature quantity represents afeature of the entire generation image. The identifier is generatedusing the total feature quantity of the generation image and a truelabel indicating whether or not the generation image is a positive imageor a negative image. In this case, a feature-point area is divided intoa plurality of small areas by separating the feature-point area as anarea having a feature point as a center in an angular direction and adistance direction on the basis of the feature point in each of aplurality of response images obtained by filtering the generation imageby a plurality of filters having different characteristics. Astatistical quantity of pixel values of a small area is produced foreach of the plurality of small areas. The statistical quantity of eachof the plurality of small areas obtained from each of the plurality ofresponse images for the feature point is set as a feature-pointfeature-quantity of the feature point. The identifier for performingidentification is generated using a dimensional feature quantity todecrease an error value representing an identification error level ofthe positive and negative images among a plurality of dimensionalfeature quantities which are components of the multi-dimensional vectoras the total feature quantity. Dimensional information representing adimension of the dimensional feature quantity to decrease the errorvalue is generated.

According to a second embodiment of the present invention, anidentification device, or a program for making a computer function as anidentification device, includes feature-point extracting means forextracting feature points as characteristic points from a processingobject image of an object used to identify whether or not a subjectviewed in the image is a predetermined identification object,feature-point feature-quantity extracting means for extractingfeature-point feature-quantities representing features of the featurepoints, dimensional-feature-quantity generating means for generating adimensional feature quantity of a dimension represented by dimensionalinformation among a plurality of dimensional feature quantities whichare components of a vector as a total feature quantity represented by amulti-dimensional vector from the feature-point feature-quantities ofthe processing object image, wherein the total feature quantityrepresents a feature of the entire processing object image, andidentification means for identifying whether or not the subject viewedin the processing object image is the predetermined identificationobject by inputting the dimensional feature quantity to an identifierfor identifying whether or not the subject viewed in the processingobject image is the predetermined identification object, wherein thefeature-point feature-quantity extracting means divides a feature-pointarea into a plurality of small areas by separating the feature-pointarea as an area having a feature point as a center in an angulardirection and a distance direction on the basis of the feature point ineach of a plurality of response images obtained by filtering theprocessing object image by a plurality of filters having differentcharacteristics, produces a statistical quantity of pixel values of asmall area for each of the plurality of small areas, and sets thestatistical quantity of each of the plurality of small areas obtainedfrom each of the plurality of response images of the processing objectimage for the feature point as a feature-point feature-quantity of thefeature point, wherein the identifier and dimensional information areobtained by extracting feature points from one of a plurality ofgeneration images including a positive image in which a predeterminedidentification object is viewed and a negative image in which noidentification object is viewed for use in learning to generate theidentifier, extracting feature-point feature-quantities representingfeatures of the feature points of the generation image, generating atotal feature quantity of the generation image from the feature-pointfeature-quantities of the generation image, and generating theidentifier using the total feature quantity of the generation image anda true label indicating whether or not the generation image is apositive image or a negative image, wherein the extracting is performedby dividing a feature-point area into a plurality of small areas byseparating the feature-point area as an area having a feature point as acenter in an angular direction and a distance direction on the basis ofthe feature point in each of a plurality of response images obtained byfiltering the generation image by the plurality of filters, producing astatistical quantity of pixel values of a small area for each of theplurality of small areas, and setting the statistical quantity of eachof the plurality of small areas obtained from each of the plurality ofresponse images for the feature point as a feature-pointfeature-quantity of the feature point, and wherein the generating isperformed by generating the identifier for performing identificationusing a dimensional feature quantity to decrease an error valuerepresenting an identification error level of the positive and negativeimages among a plurality of dimensional feature quantities which arecomponents of the multi-dimensional vector as the total featurequantity, and generating dimensional information representing adimension of the dimensional feature quantity to decrease the errorvalue.

According to the second embodiment of the present invention, anidentification method includes the steps of extracting feature points ascharacteristic points from a processing object image of an object usedto identify whether or not a subject viewed in the image is apredetermined identification object, extracting feature-pointfeature-quantities representing features of the feature points,generating a dimensional feature quantity of a dimension represented bydimensional information among a plurality of dimensional featurequantities which are components of a vector as a total feature quantityrepresented by a multi-dimensional vector from the feature-pointfeature-quantities of the processing object image, wherein the totalfeature quantity represents a feature of the entire processing objectimage, and identifying whether or not the subject viewed in theprocessing object image is the predetermined identification object byinputting the dimensional feature quantity to an identifier foridentifying whether or not the subject viewed in the processing objectimage is the predetermined identification object, wherein thefeature-point feature-quantity extracting step includes dividing afeature-point area into a plurality of small areas by separating thefeature-point area as an area having a feature point as a center in anangular direction and a distance direction on the basis of the featurepoint in each of a plurality of response images obtained by filteringthe processing object image by a plurality of filters having differentcharacteristics, producing a statistical quantity of pixel values of asmall area for each of the plurality of small areas, and setting thestatistical quantity of each of the plurality of small areas obtainedfrom each of the plurality of response images of the processing objectimage for the feature point as a feature-point feature-quantity of thefeature point, wherein the identifier and dimensional information areobtained by extracting feature points from one of a plurality ofgeneration images including a positive image in which a predeterminedidentification object is viewed and a negative image in which noidentification object is viewed for use in learning to generate theidentifier, extracting feature-point feature-quantities representingfeatures of the feature points of the generation image, generating atotal feature quantity of the generation image from the feature-pointfeature-quantities of the generation image, and generating theidentifier using the total feature quantity of the generation image anda true label indicating whether or not the generation image is apositive image or a negative image, wherein the extracting is performedby dividing a feature-point area into a plurality of small areas byseparating the feature-point area as an area having a feature point as acenter in an angular direction and a distance direction on the basis ofthe feature point in each of a plurality of response images obtained byfiltering the generation image by the plurality of filters, producing astatistical quantity of pixel values of a small area for each of theplurality of small areas, and setting the statistical quantity of eachof the plurality of small areas obtained from each of the plurality ofresponse images for the feature point as a feature-pointfeature-quantity of the feature point, and wherein the generating isperformed by generating the identifier for performing identificationusing a dimensional feature quantity to decrease an error valuerepresenting an identification error level of the positive and negativeimages among a plurality of dimensional feature quantities which arecomponents of the multi-dimensional vector as the total featurequantity, and generating dimensional information representing adimension of the dimensional feature quantity to decrease the errorvalue.

In the second embodiment of the present invention, feature points ascharacteristic points are extracted from a processing object image of anobject used to identify whether or not a subject viewed in the image isa predetermined identification object. Feature-point feature-quantitiesrepresenting features of the feature points are extracted. A dimensionalfeature quantity of a dimension represented by dimensional informationis generated among a plurality of dimensional feature quantities whichare components of a vector as a total feature quantity represented by amulti-dimensional vector from the feature-point feature-quantities ofthe processing object image, wherein the total feature quantityrepresents a feature of the entire processing object image. It isidentified whether or not the subject viewed in the processing objectimage is the predetermined identification object by inputting thedimensional feature quantity to an identifier for identifying whether ornot the subject viewed in the processing object image is thepredetermined identification object. In this case, a feature-point areais divided into a plurality of small areas by separating thefeature-point area as an area having a feature point as a center in anangular direction and a distance direction on the basis of the featurepoint in each of a plurality of response images obtained by filteringthe processing object image by a plurality of filters having differentcharacteristics. A statistical quantity of pixel values of a small areais produced for each of the plurality of small areas. The statisticalquantity of each of the plurality of small areas obtained from each ofthe plurality of response images of the processing object image for thefeature point is produced as a feature-point feature-quantity of thefeature point. The identifier and dimensional information are obtainedby extracting feature points from one of a plurality of generationimages including a positive image in which a predeterminedidentification object is viewed and a negative image in which noidentification object is viewed for use in learning to generate theidentifier, extracting feature-point feature-quantities representingfeatures of the feature points of the generation image, generating atotal feature quantity of the generation image from the feature-pointfeature-quantities of the generation image, and generating theidentifier using the total feature quantity of the generation image anda true label indicating whether or not the generation image is apositive image or a negative image, wherein the extracting is performedby dividing a feature-point area into a plurality of small areas byseparating the feature-point area as an area having a feature point as acenter in an angular direction and a distance direction on the basis ofthe feature point in each of a plurality of response images obtained byfiltering the generation image by the plurality of filters, producing astatistical quantity of pixel values of a small area for each of theplurality of small areas, and setting the statistical quantity of eachof the plurality of small areas obtained from each of the plurality ofresponse images for the feature point as a feature-pointfeature-quantity of the feature point, and wherein the generating isperformed by generating the identifier for performing identificationusing a dimensional feature quantity to decrease an error valuerepresenting an identification error level of the positive and negativeimages among a plurality of dimensional feature quantities which arecomponents of the multi-dimensional vector as the total featurequantity, and generating dimensional information representing adimension of the dimensional feature quantity to decrease the errorvalue.

Each of the learning device and the identification device can be anindependent device, or can be an internal block constituting one device.

The program can be provided by transmitting the program via atransmission medium or recording the program on a recording medium.

According to the first and second embodiments of the present invention,both discrimination and invariance can be improved in the identificationof whether or not a subject viewed in an image is a predeterminedidentification object.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a configuration example of alearning device according to an embodiment of the present invention;

FIG. 2 is a block diagram illustrating a configuration example of afeature-point feature-quantity extracting section;

FIG. 3 is a diagram illustrating the derivatives of a Gaussian function;

FIG. 4 is a diagram illustrating the derivatives of a Gaussian function;

FIG. 5 is a diagram illustrating the derivatives of a Gaussian function;

FIG. 6 is a diagram illustrating response images;

FIG. 7 is a diagram illustrating a feature-point area;

FIG. 8 is a diagram illustrating one type of feature quantity;

FIG. 9 is a flowchart illustrating a feature-point feature-quantityextracting process;

FIGS. 10A and 10B are diagrams illustrating a process of atotal-feature-quantity generating section;

FIG. 11 is a flowchart illustrating a total-feature-quantity generatingprocess;

FIG. 12 is a flowchart illustrating a total-feature-quantity generatingprocess;

FIG. 13 is a diagram illustrating a process of an identifier generatingsection;

FIG. 14 is a flowchart illustrating an identifier generating process;and

FIG. 15 is a flowchart illustrating a learning process of a learningdevice.

FIG. 16 is a block diagram illustrating a configuration example of anidentification device according to an embodiment of the presentinvention;

FIG. 17 is a flowchart illustrating an identification process of theidentification device; and

FIG. 18 is a block diagram illustrating a configuration example of acomputer according to an embodiment of the present invention.

DESCRIPTION OF THE PREFERRED EMBODIMENTS Configuration Example ofLearning Device According to Embodiment

FIG. 1 is a block diagram illustrating a configuration example of alearning device according to an embodiment of the present invention.

In FIG. 1, the learning device generates (produces) an identifier foridentifying whether or not a subject viewed in an image is apredetermined identification object and dimensional information to bedescribed later using learning images and true labels.

Here, the learning images are images used for generating (learning) anidentifier, and include a plurality of model images and a plurality ofgeneration images.

The model image is a positive image in which an identification object isviewed, and the generation image includes both a positive image and anegative image in which no identification object is viewed (an objectother than the identification object is viewed).

The true label exists for every generation image, and is a labelindicating whether each generation image is the positive or negativeimage.

In FIG. 1, the learning device includes a feature-point extractingsection 11, a feature-point feature-quantity extracting section 12, afeature-point feature-quantity storage section 13, a feature-pointextracting section 21, a feature-point feature-quantity extractingsection 22, a total-feature-quantity generating section 23, and anidentifier generating section 24.

A model image of the learning images is supplied from the outside to thefeature-point extracting section 11. The feature-point extractingsection 11 extracts feature points as characteristic points from themodel image supplied thereto, and supplies the feature-pointfeature-quantity extracting section 12 with the extracted feature pointsand the model image.

Here, the feature-point extracting section 11 extracts (a pixel at) acorner point as the feature point since local information of the imageis usually included at the corner point.

The corner point can be extracted using a Harris corner detector. When apixel value of a pixel (for example, luminance) at a certain position(x,y) is denoted by I(x,y) in the Harris corner detector, a pixel inwhich two unique values of the second-order moment L of the luminancegradient produced by Expression (1) are equal to or greater than athreshold value that is detected as the corner point.

$\begin{matrix}{L = \begin{bmatrix}\left( \frac{I}{x} \right)^{2} & {\left( \frac{I}{x} \right)\left( \frac{I}{y} \right)} \\{\left( \frac{I}{x} \right)\left( \frac{I}{y} \right)} & \left( \frac{I}{y} \right)^{2}\end{bmatrix}} & {{Expression}\mspace{14mu} 1}\end{matrix}$

In Expression (1), a pixel value I(x,y) is expressed as I by omitting(x,y).

In addition, for example, it is possible to adopt a pixel at an edge, apixel at a predetermined fixed position, or the like as the featurepoint.

The feature-point feature-quantity extracting section 12 extracts afeature-point feature-quantity representing a feature of the samefeature point from the model image supplied by the feature-pointextracting section 11, and supplies the feature-point feature-quantitystorage section 13 with the extracted feature-point feature-quantity.

The feature-point feature-quantity storage section 13 stores thefeature-point feature-quantity of the model image from the feature-pointfeature-quantity extracting section 12.

A generation image of the learning images is supplied from the outsideto the feature-point extracting section 21. Like the feature-pointextracting section 11, the feature-point extracting section 21 extractsfeature points from the supplied generation image, and supplies thefeature-point feature-quantity extracting section 22 with the extractedfeature points and the generation image.

Like the feature-point feature-quantity extracting section 12, thefeature-point feature-quantity extracting section 22 extracts afeature-point feature-quantity representing a feature of the samefeature point from the generation image supplied by the feature-pointextracting section 21, and supplies the total-feature-quantitygenerating section 23 with the extracted feature-point feature-quantity.

The total-feature-quantity generating section 23 produces a totalfeature quantity indicating a feature of the entire generation imagefrom feature-point feature-quantities of the generation image suppliedby the feature-point feature-quantity extracting section 22 on the basisof feature-point feature-quantities of the model image stored in thefeature-point feature-quantity storage section 13.

Here, for example, the total feature quantity is expressed by amulti-dimensional vector (a vector having a plurality of values ascomponents). The total feature quantity as the multi-dimensional vectoris supplied from the total-feature-quantity generating section 23 to theidentifier generating section 24.

Not only the total feature quantity of the generation image is suppliedfrom the total-feature-quantity generating section 23 to the identifiergenerating section 24, but also a true label of the generation image issupplied from the outside to the identifier generating section 24. Theidentifier generating section 24 generates an identifier using the totalfeature quantity of the generation image and the true label of thegeneration image (performs learning to produce a parameter defining theidentifier).

Here, when the components of the multi-dimensional vector as the totalfeature quantity are assumed to be dimensional feature quantities, thetotal feature quantity includes a plurality of dimensional featurequantities (whose number corresponds to that of vector dimensions).

The identifier generating section 24 generates an identifier for anidentification process using partial dimensional feature quantitiesselected from among dimensional feature quantities constituting thetotal feature quantity without employing all the dimensional featurequantities constituting the total feature quantity. Informationindicating a dimension of a dimensional feature quantity to be used foridentification by the identifier (information indicating a sequencenumber of a component of the vector as the total feature quantity) isdimensional information, and the identifier generating section 24 alsogenerates the dimensional information along with the identifier.

Configuration Example of Feature-Point Feature-Quantity ExtractingSection 12

FIG. 2 is a block diagram illustrating a configuration example of thefeature-point feature-quantity extracting section 12 of FIG. 1.

The feature-point feature-quantity extracting section 22 of FIG. 1 and afeature-point feature-quantity extracting section 72 of anidentification device (FIG. 16) also have the same configuration as thefeature-point feature-quantity extracting section 12. In this regard,the feature-point feature-quantity extracting section 12 processes amodel image as an object, but the feature-point feature-quantityextracting section 22 processes a generation image as an object and thefeature-point feature-quantity extracting section 72 processes aprocessing object image as an object.

In FIG. 2, the feature-point feature-quantity extracting section 12includes a filter section 41 and a feature-point feature-quantitycalculating section 42.

An object image from which a feature-point feature-quantity isextracted, that is, a model image here, is supplied from thefeature-point feature-quantity extracting section 11 (FIG. 1) to thefilter section 41.

The filter section 41 filters the model image from the feature-pointextracting section 11 using a plurality of filters having differentcharacteristics, and supplies the feature-point feature-quantitycalculating section 42 with a plurality of response images (filteringresults) obtained as results of filtering.

The feature points of the model image from the feature-point extractingsection 11 (FIG. 1) as well as the response images of the model imagefrom the filter section 41 are supplied to the feature-pointfeature-quantity calculating section 42.

The feature-point feature-quantity calculating section 42 sets afeature-point area as an area having the center of the feature pointfrom the feature-point extracting section 11 for each of the pluralityof response images of the model image from the filter section 41. Thefeature-point feature-quantity calculating section 42 divides thefeature-point area into a plurality of small areas by separating thefeature-point area in an angular direction and a distance direction onthe basis of the feature point.

The feature-point feature-quantity calculating section 42 produces astatistical quantity of pixel values (pixel values of a small area) foreach of the plurality of small areas, and outputs a statistical quantityof each of the plurality of small areas obtained from each of theplurality of response images for each feature point as a feature-pointfeature-quantity of the feature point.

Filtering of the filter section 41 of FIG. 2 will be described withreference to FIGS. 3 to 6.

For example, the filter section 41 produces a response of a steerablefilter disclosed in J. J. Yokono and T. Poggio, “Oriented Filters forObject Recognition: an empirical study”, FG 2004 as a response image byfiltering the model image from the feature-point extracting section 11.

That is, for example, the filter section 41 filters the model image fromthe feature-point extracting section 11 by each of a plurality ofderivatives based on Gaussian functions of a plurality of scales σ, aplurality of angle θ directions, and a plurality of differentiations cas a plurality of filters having different characteristics, and outputsa plurality of response images of the model image.

Specifically, a two-dimensional Gaussian function G(x,y) is expressed byExpression (2) using a scale (standard deviation) σ.

$\begin{matrix}{{G\left( {x,y} \right)} = ^{- \frac{x^{2} + y^{2}}{2\sigma^{2}}}} & {{Expression}\mspace{14mu} 2}\end{matrix}$

A derivative based on a Gaussian function G(x,y), an angle θ [degree]direction, and c differentiations (a derivative obtained by performingthe c differentiations of the Gaussian function G(x,y)) (hereinafter,also referred to as a “c^(th)-order derivative”) is expressed by G_(c)^(θ).

Since a first-order derivative G₁ ^(0°) of the 0 degree direction of theGaussian function G(x,y) matches a result of the (partial)differentiation of the x direction of the Gaussian function G(x,y), thefirst-order derivative G₁ ^(0°) can be expressed by Expression (3).

G 1 0 ° =   x   - x 2 + y 2 2  σ 2 = - x σ 2   - x 2 + y 2 2  σ2  Expression   3

Since a first-order derivative G₁ ^(90°) of the 90 degree direction ofthe Gaussian function G(x,y) matches a result of the differentiation ofthe y direction of the Gaussian function G(x,y), the first-orderderivative G₁ ^(90°) can be expressed by Expression (4).

G 1 90 ° =  y   - x 2 + y 2 2  σ 2 = - y σ 2   - x 2 + y 2 2  σ 2 Expression   4

The first-order derivative G₁ ^(0°) of Expression (3) and thefirst-order derivative G₁ ^(90°) of Expression (4) are basis functionsof a first-order derivative G₁ ^(θ) of an arbitrary angle θ direction ofthe Gaussian function G(x,y). Therefore, the first-order derivative G₁^(θ) of the arbitrary angle θ direction of the Gaussian function G(x,y)can be expressed by Expression (5) as a linear combination of thefirst-order derivatives G₁ ^(0°) and G₁ ^(90°) which are the basisfunctions.

G ₁ ^(θ)=cos(θ)G ₁ ^(0°)+sin(θ)G ₁ ^(90°)  Expression 5

From Expression (5), for example, a first-order derivative G₁ ^(45°) ofthe 45 degree direction of the Gaussian function G(x,y) is expressed byexpression (6).

G ₁ ⁴⁵=cos(45°)G ₁ ^(0°)+sin(45°)G ₁ ^(90°)  Expression 6

Here, FIG. 3 illustrates the first-order derivatives G₁ ^(0°) and G₁^(90°) as the basis functions, and the first-order derivative G₁ ^(45°)of the 45 degree direction.

In FIG. 3 (like FIGS. 4 and 5 to be described later), the x direction isthe transverse direction and the y direction is the longitudinaldirection. The lighter (or darker) the color, the larger (or smaller)the value.

The first-order derivative G₁ ^(90°) as the basis function is obtainedby rotating the first-order derivative G₁ ^(0°) as the basis function by90 degrees (in a counterclockwise rotation) with respect to the origin.Likewise, the first-order derivative G₁ ^(45°) is obtained by rotatingthe first-order derivative G₁ ^(0°) by 45 degrees.

For example, the filter section 41 produces 8 response images byfiltering the model image in each of first-order derivatives G₁ ^(θ)based on Gaussian functions G(x,y) of two scales σ=1 and 2, four angleθ=θ_(A), θ_(B), θ_(C), and θ_(D) directions and the number ofdifferentiations c=1.

Here, since the filtering of the model image by the first-orderderivative G₁ ^(θ) is expressed by the convolution of the first-orderderivative G₁ ^(θ) and the model image, it can be expressed by a linearcombination of convolution results of the first-order derivatives G₁^(0°) and G₁ ^(90°) as the basis functions and the model image fromExpression (5).

When a pixel value of the model image is expressed by I, a convolutionresult R₁ ^(0°) of the first-order derivative G₁ ^(0°) and the modelimage I is expressed by Expression (7) and a convolution result R₁^(90°) of the first-order derivative G₁ ^(90°) and the model image I isexpressed by Expression (8).

R ₁ ^(0°) =G ₁ ^(0°) *I  Expression 7

R ₁ ^(90°) =G ₁ ^(90°) *I  Expression 8

Here, * represents the convolution.

The response image R₁ ^(θ) obtained by filtering the model image I inthe first-order derivative G₁ ^(θ) is expressed by Expression (9) usingthe convolution result R₁ ^(0°) of Expression (7) and the convolutionresult R₁ ^(90°) of Expression (8).

R ₁ ^(θ)=cos(θ)R ₁ ^(0°)+sin(θ)R ₁ ^(90°)  Expression 9

The filter section 41 performs a filtering process for each of asecond-order derivative G₂ ^(θ) of the number of differentiations c=2and a third-order derivative G₃ ^(θ) of the number of differentiationsc=3 as in the first-order derivative G₁ ^(θ) of the number ofdifferentiations c=1. The filter section 41 produces 8 response imagesfrom the second-order derivative G₂ ^(θ) and produces 8 response imagesfrom the third-order derivative G₃ ^(θ).

Here, the second-order derivative G₃ ^(θ) can be expressed by Expression(10) using three second-order derivatives G₂ ^(0°), G₂ ^(60°), and G₂^(120°).

G ₂ ^(θ) =k ₂₁(θ)G ₂ ^(0°) +k ₂₂(θ)G ₂ ^(60°) +k ₂₃(θ)G ₂^(120°)  Expression 10

A coefficient k_(2i)(θ) of Expression (10) is expressed by Expression(11).

$\begin{matrix}{{k_{2i}(\theta)} = {\frac{1}{3}\left\{ {1 + {2{\cos \left( {2\left( {\theta - \theta_{i}} \right)} \right)}}} \right\}}} & {{Expression}\mspace{14mu} 11}\end{matrix}$

In this regard, in Expression (11), θ₁, θ₂, and θ₃ are 0 degree, 60degrees, and 120 degrees, respectively.

Here, FIG. 4 illustrates the three second-order derivatives G₂ ^(0°), G₂^(60°), and G₂ ^(120°) as the basis functions of the second-orderderivative G₂ ^(θ).

The second-order derivatives G₂ ^(60°) is obtained by rotating thesecond-order derivatives G₂ ^(0°) by 60 degrees, and the second-orderderivatives G₂ ^(120°) is obtained by rotating the second-orderderivatives G₂ ^(0°) by 120 degrees.

The third-order derivative G₃ ^(θ) can be expressed by Expression (12)using 4 third-order derivatives G₃ ^(0°), G₃ ^(45°), G₃ ^(90°), and G₃^(135°) as the basis functions.

G ₃ ^(θ) =k ₃₁(θ)G ₃ ^(0°) +k ₃₂(θ)G ₂ ^(45°) +k ₃₃(θ)G ₂ ^(90°) +k₃₄(θ)G ₂ ^(135°)  Expression 12

A coefficient k_(3i)(θ) of Expression (12) is expressed by Expression(13).

$\begin{matrix}{{k_{3i}(\theta)} = {\frac{1}{4}\left\{ {{2{\cos \left( {\theta - \theta_{i}} \right)}} + {2{\cos \left( {3\left( {\theta - \theta_{i}} \right)} \right)}}} \right\}}} & {{Expression}\mspace{14mu} 13}\end{matrix}$

In this regard, in Expression (13), θ₁, θ₂, θ₃, and θ₄ are 0 degree, 45degrees, 90 degrees, and 135 degrees, respectively.

Here, FIG. 5 illustrates the 4 third-order derivatives G₃ ^(0°), G₃^(45°), G₃ ^(90°), and G₃ ^(135°) as the basis functions of thethird-order derivative G₃ ^(θ).

The third-order derivatives G₃ ^(45°) is obtained by rotating thethird-order derivatives G₃ ^(0°) by 45 degrees, the third-orderderivatives G₃ ^(90°) is obtained by rotating the third-orderderivatives G₃ ^(0°) by 90 degrees, and the third-order derivatives G₃^(135°) is obtained by rotating the third-order derivatives G₃ ^(0°) by135 degrees.

FIG. 6 illustrates response images of the model image output from thefilter section 41 of FIG. 2.

The filter section 41 filters the model image in the first-orderderivative G₁ ^(θ), the second-order derivative G₂ ^(θ), and thethird-order derivative G₃ ^(θ) as derivatives based on Gaussianfunctions G(x,y) of two scales σ=1 and 2, four angle θ=θ_(A), θ_(B),θ_(C), and θ_(D) directions, and three differentiations c=1, 2, and 3.

Therefore, the filter section 41 produces a number of combinations basedon the two scales σ=1 and 2, the four angle θ=θ_(A), θ_(B), θ_(C), andθ_(D) directions, and the three differentiations c=1, 2, and 3 from onemodel image, that is, 24 response images, and supplies the responseimages to the feature-point feature-quantity calculating section 42.

A function used as a filter in the filter section 41 is not limited to aGaussian function. In FIGS. 3 to 6, the derivatives based on theGaussian functions G(x,y) of two scales σ=1 and 2, four angle θ=θ_(A),θ_(B), θ_(C), and θ_(D) directions, and three differentiations c=1, 2,and 3 have been adopted as filters. The scale σ, the angle θ, and thenumber of differentiations c are not limited to the above-describedvalues. It is possible to adopt a function other than the derivative ofthe Gaussian function G(x,y).

Next, a process of the feature-point feature-quantity calculatingsection 42 of FIG. 2 will be described with reference to FIGS. 7 and 8.

As described with reference to FIG. 6, the filter section 41 produces 24response images from one model image and supplies the response images tothe feature-point feature-quantity calculating section 42.

Among the 24 response images produced from one model image, one responseimage is observed. Among feature points (to be exact, points on theimage having the same positions as feature points of the model image) ofthe observed response image (hereinafter, also referred to asobservation response image), one feature point is observed.

The feature-point feature-quantity calculating section 42 sets afeature-point area having the center of the observed feature point(hereinafter, also referred to as observation feature point) amongfeature points from the feature-point extracting section 11 (FIG. 1) forthe observation response image. The feature-point feature-quantitycalculating section 42 divides the feature-point area into a pluralityof small areas by separating the feature-point area in an angulardirection and a distance direction on the basis of the observationfeature point.

FIG. 7 illustrates a feature-point area and a plurality of small areasinto which the feature-point area is divided.

For example, the feature-point feature-quantity calculating section 42sets a circular area having the center of an observation feature pointand having a fixed radius to the feature-point area, and divides thefeature-point area into a plurality of small areas by separating thefeature-point area in an angular direction and a distance direction onthe basis of the observation feature point.

In FIG. 7, the feature-point area is separated in 8 angular directionsand 3 distance directions, and is divided into 24 small areas in total.

The number of angular directions or the number of distance directions bywhich the feature-point area is separated is not particularly limited.

For example, the feature-point feature-quantity calculating section 42produces an average value of pixel values of a small area (pixel valuesof pixels within the small area) as a statistical quantity of the smallarea for each of the 24 small areas obtained for the observation featurepoint.

The feature-point feature-quantity calculating section 42 produces 6types of feature quantities corresponding to a number of combinations oftwo scales σ=1 and 2 and three differentiations c=1, 2, and 3 asfeature-point feature-quantities by employing a vector having componentsof average values of pixel values of small areas produced from 4response images obtained by filtering in each of derivatives based on aGaussian function G(x,y) of the same scale σ, four angle θ=θ_(A), θ_(B),θ_(C), and θ_(D) directions and the same number of differentiations c asone type of feature quantity of the observation feature point.

FIG. 8 is a diagram illustrating one type of feature quantity producedby the feature-point feature-quantity calculating section 42.

In FIG. 8, average values of pixel values of 24 small areas per responseimage (hereinafter, also referred to as small-area average values) areproduced for an observation feature point from each of 4 response imagesobtained by filtering in each of derivatives (first-order derivatives)based on a Gaussian function G(x,y) of a scale σ=1, four angle θ=θ_(A),θ_(B), θ_(C), and θ_(D) directions and the number of differentiationsc=1.

In total, 96 small-area average values are produced from 4 responseimages obtained by filtering in each of the derivatives based on aGaussian function G(x,y) of the same scale σ, four angle θ=θ_(A), θ_(B),θ_(C), and θ_(D) directions, and the same number of differentiations c.

The feature-point feature-quantity calculating section 42 uses a96-dimensional vector having components of the 96 small-area averagevalues as one type of feature quantity of the observation feature point.

In addition to the case where the scale σ=1 and the number ofdifferentiations c=1, the feature-point feature-quantity calculatingsection 42 produces a 96-dimensional vector having components of the 96small-area average values as one type of feature quantity of theobservation feature point even for each of the cases where the scale σ=1and the number of differentiations c=2, the case where the scale σ=1 andthe number of differentiations c=3, the case where the scale σ=2 and thenumber of differentiations c=1, the case where the scale σ=2 and thenumber of differentiations c=2, and the case where the scale σ=2 and thenumber of differentiations c=3.

Consequently, the feature-point feature-quantity calculating section 42produces 6 types of feature quantities (six 96-dimensional vectors) asfeature-point feature-quantities of the observation feature point.

As described above, the feature-point feature-quantity extractingsection 12 produces a vector having components of average values ofpixel values of small areas produced from a response image obtained byfiltering in each of derivatives based on Gaussian functions G(x,y) of aplurality of scales σ=1 and 2, a plurality of angle θ=θ_(A), θ_(B),θ_(C), and θ_(D) directions, and a plurality of differentiations c=1, 2,and 3 as a feature-point feature-quantity of the observation featurepoint.

Here, in the related art of J. J. Yokono and T. Poggio, “OrientedFilters for Object Recognition: an empirical study”, FG 2004, afeature-point feature-quantity having high discrimination is produced bysetting a vector having components of pixel values of feature points ofa plurality of response images obtained by filtering in each ofderivatives based on Gaussian functions G(x,y) of a plurality of scalesσ and a plurality of angle θ directions as a feature-pointfeature-quantity.

On the other hand, the learning device of FIG. 1 produces feature-pointfeature-quantities from a plurality of response images corresponding tocombinations of a plurality of scales σ, a plurality of angle θdirections, and a plurality of the derivatives by filtering a modelimage in each of the derivatives based on Gaussian functions G(x,y) of aplurality of scales σ=1 and 2, a plurality of angle θ=θ_(A), θ_(B),θ_(C), and θ_(D) directions, and a plurality of differentiations c=1, 2,and 3. Therefore, a feature-point feature-quantity with higherdiscrimination in which change states of a pixel value in variousdirections, that is, information of various textures of an image, havebeen reflected can be obtained from the image (here, the model image).

The learning device of FIG. 1 uses an average value of pixel values of aplurality of small areas obtained by separating a feature-point areahaving a center of a feature point in a response image in an angulardirection and a distance direction on the basis of the feature point asa feature-point feature-quantity.

Therefore, a feature-point feature-quantity having high discriminationcan be obtained in which a peripheral distribution has been reflected inresponses of a plurality of filters with different characteristics ineach of the derivatives based on Gaussian functions G(x,y) of aplurality of scales σ=1 and 2, a plurality of angle θ=θ_(A), θ_(B),θ_(C), and θ_(D) directions, and a plurality of differentiations c=1, 2,and 3.

A feature-point feature-quantity robust to the tilt (rotation) ordistortion of a subject viewed in an image, that is, a feature-pointfeature-quantity having improved invariance, can be obtained byproducing feature-point feature-quantities from a plurality of smallareas around a feature point and setting an average value as astatistical quantity of pixel values of a small area to a feature-pointfeature-quantity.

Description of Feature-Point Feature-Quantity Extracting Process

A feature-point feature-quantity extracting process in which thefeature-point feature-quantity extracting section 12 of FIG. 2 extractsa feature-point feature-quantity will be described with reference toFIG. 9.

In step S11, the feature-point feature-quantity extracting section 12selects one model image, which has not yet been selected as anobservation image from model images supplied by the feature-pointextracting section 11 (FIG. 1), as the observation image. Then, theprocess proceeds to step S12.

In step S12, the filter section 41 (FIG. 2) of the feature-pointfeature-quantity extracting section 12 filters the observation image ineach of the derivatives based on Gaussian functions G(x,y) of two scalesσ=1 and 2, four angle θ=θ_(A), θ_(B), θ_(C), and θ_(D) directions, andthree differentiations c=1, 2, and 3. That is, the filter section 41produces 24 response images by filtering the observation image asillustrated in FIG. 6.

The filter section 41 supplies the feature-point feature-quantitycalculating section 42 with the 24 response images produced from theobservation image, and the process proceeds from step S12 to step S13.

In step S13, the feature-point feature-quantity calculating section 42selects as an observation feature point one feature point which has notbeen selected as an observation feature point from feature points of theobservation image included in feature points of the model image suppliedfrom the feature-point extracting section 11. The process proceeds tostep S14.

In step S14, the feature-point feature-quantity calculating section 42sets a feature-point area having the center of the observation featurepoint in a response image obtained by filtering in the derivative basedon a Gaussian function G(x,y) of the same scale σ, the same angle θdirection, and the same number of differentiations c, that is, each of24 response images from the filter section 41.

Then, the process proceeds from step S14 to step S15, and thefeature-point feature-quantity calculating section 42 divides thefeature-point area of the response image into 24 small areas byseparating the feature-point area of the response image in an angulardirection and a distance direction on the basis of the observationfeature point in each of the 24 response images as illustrated in FIG.7.

The process proceeds from step S15 to step S16, and the feature-pointfeature-quantity calculating section 42 produces a small-area averagevalue of each of the 24 small areas obtained by dividing thefeature-point area of the observation feature point in each of the 24response images. The process proceeds to step S17.

In step S17, the feature-point feature-quantity calculating section 42produces 6 types of feature quantities corresponding to a number ofcombinations of two scales σ=1 and 2 and three differentiations c=1, 2,and 3 as feature-point feature-quantities of the observation featurepoint by employing a vector having components of average values of pixelvalues of small areas produced from response images obtained byfiltering in each of derivatives based on a Gaussian function G(x,y) ofthe same scale σ, four angle θ=θ_(A), θ_(B), θ_(C), and θ_(D)directions, and the same number of differentiations c as one type offeature quantity of the observation feature point.

Then, the process proceeds from step S17 to step S18, and thefeature-point feature-quantity extracting section 12 determines whetherall feature-point feature-quantities of feature points of theobservation image have been produced. When all the feature-pointfeature-quantities of the feature points of the observation image havenot yet been produced in step S18, that is, when there is a featurepoint which has not yet been selected as an observation feature pointamong the feature points of the observation image, the process returnsto step S13.

In step S13, the feature-point feature-quantity calculating section 42newly selects one feature point which has not yet been selected as theobservation feature point from the feature points of the observationimage. In the following, the same process is repeated.

When it has been determined that all the feature-pointfeature-quantities of the feature points of the observation image havebeen produced in step S18, the process proceeds to step S19. Thefeature-point feature-quantity extracting section 12 determines whetherfeature-point feature-quantities for all model images from thefeature-point extracting section 11 (FIG. 1) have been produced.

When the feature-point feature-quantities for all the model images fromthe feature-point extracting section 11 have not yet been produced instep S19, that is, when there is a model image which has not yet beenselected as the observation image among the model images from thefeature-point extracting section 11, the process returns to step S11.

In step S11, the feature-point feature-quantity extracting section 12newly selects one model image which has not yet been selected as theobservation image from the model images from the feature-pointextracting section 11. In the following, the same process is repeated.

When it has been determined that the feature-point feature-quantitiesfor all the model images from the feature-point extracting section 11have been produced in step S19, the feature-point feature-quantityextracting process is terminated.

The feature-point feature-quantity extracting section 12 supplies thefeature-point feature-quantity storage section 13 (FIG. 1) with thefeature-point feature-quantities of the model images produced by thefeature-point feature-quantity extracting process, and the feature-pointfeature-quantity storage section 13 stores the feature-pointfeature-quantities.

The feature-point feature-quantity extracting section 12 can performvector quantization for the feature-point feature-quantities of themodel images, and can store vector quantization results (codes) astarget feature-point feature-quantities of the model images in thefeature-point feature-quantity storage section 13.

Here, (the feature-point feature-quantity calculating section 42 of) thefeature-point feature-quantity extracting section 12 produces 6 types offeature quantities (six 96-dimensional vectors) using a 96-dimensionalvector, having 96 small-area average values as components for onefeature point, as one type of feature quantity of the observationfeature point as described with reference to FIGS. 8 and 9.

When vector quantization of a feature-point feature-quantity isperformed in the feature-point feature-quantity extracting section 12,the vector quantization is performed for every type of feature quantity(96-dimensional vector).

Here, a codebook is necessary for the vector quantization, but thecodebook can be generated, for example, by a k-means algorithm or thelike. As in the vector quantization, 6 types of codebooks are generatedby generating a codebook for every type of feature quantity(96-dimensional vector). The number of code vectors of the codebook (thenumber of clusters of vector quantization) may be, for example, 400 orthe like.

Description of Process of Total-Feature-Quantity Generating Section 23

A process to be executed by the total-feature-quantity generatingsection 23 of FIG. 1 will be described with reference to FIG. 10.

In the learning device of FIG. 1, the feature-point extracting section11 and the feature-point feature-quantity extracting section 12 producefeature-point feature-quantities of a model image and store thefeature-point feature-quantities in the feature-point feature-quantitystorage section 13 as described above.

Like the feature-point extracting section 11 and the feature-pointfeature-quantity extracting section 12, the feature-point extractingsection 21 and the feature-point feature-quantity extracting section 22produce feature-point feature-quantities of a generation image andsupply the feature-point feature-quantities to thetotal-feature-quantity generating section 23.

The total-feature-quantity generating section 23 produces a totalfeature quantity indicating a feature of the entire generation image (arelative feature based on the model image) from the feature-pointfeature-quantities of the generation image supplied by the feature-pointfeature-quantity extracting section 22 on the basis of the feature-pointfeature-quantities stored in the feature-point feature-quantity storagesection 13.

FIG. 10 illustrates an example of a total feature quantity produced bythe total-feature-quantity generating section 23.

For example, the total-feature-quantity generating section 23 canproduce a histogram of feature-point feature-quantity values of ageneration image from the feature-point feature-quantity extractingsection 22 in which feature-point feature-quantity values as values offeature-point feature-quantities of a model image stored in thefeature-point feature-quantity storage section 13 (FIG. 1) are ranked,as a total feature quantity of the generation image.

For example, the total-feature-quantity generating section 23 canproduce a correlation value of feature-point feature-quantity values ofthe generation image from the feature-point feature-quantity extractingsection 22 to feature-point feature-quantities of the model image storedin the feature-point feature-quantity storage section 13 as a totalfeature quantity of the generation image.

FIG. 10A illustrates a histogram of feature-point feature-quantityvalues produced as a total feature quantity (hereinafter, also referredto as a feature-point feature-quantity value histogram) for each ofpositive and negative images included in generation images.

FIG. 10B illustrates a correlation value of feature-pointfeature-quantity values produced as a total feature quantity(hereinafter, also referred to as a feature-point feature-quantitycorrelation value) for each of the positive and negative images includedin the generation images.

The feature-point feature-quantity value histogram of FIG. 10A can beproduced as follows.

That is, for simplification of the description, it is assumed that afeature-point feature-quantity is not 6 types of feature quantities, butis one type of feature quantity (96-dimensional vector).

The number of feature-point feature-quantity values (types) stored inthe feature-point feature-quantity storage section 13 (FIG. 1) isassumed to be K.

When a certain feature point of a generation image is observed, thetotal-feature-quantity generating section 23 increments the frequency ofa rank closest to a feature-point feature-quantity value of the observedfeature point (observation feature point) in the generation image amongK ranks (feature-point feature-quantity values) using the Kfeature-point feature-quantity values (96-dimensional vectors) stored inthe feature-point feature-quantity storage section 13 as the ranks (thehorizontal axis of the histogram) by 1.

The total-feature-quantity generating section 23 counts the frequenciesof the K ranks using feature-point feature-quantity values of allfeature points of the generation image as objects, and outputs aK-dimensional vector, having the frequencies of the K ranks of thehistogram (feature-point feature-quantity value histogram) obtainedthereby as components, as a total feature quantity of the generationimage.

When the feature-point feature-quantity is 6 types of feature quantities(96-dimensional vectors) as described above, a feature-pointfeature-quantity value histogram is produced for every type and a6×K-dimensional vector having a total of frequencies of 6×K ranks of 6feature-point feature-quantity value histograms in 6 types as componentsis regarded as a total feature quantity of the generation image.

Here, the feature-point feature-quantity value histogram as the totalfeature quantity is a BoF histogram (G. Csurka, C. Bray, C. Dance, andL. Fan, “Visual Categorization with Bags of Keypoints”, ECCV 2004), andrepresents an extent in which a feature-point feature-quantity valuepresent in the model image exists in the generation image.

A feature-point feature-quantity correlation value of FIG. 10B can beproduced as follows.

That is, here, for simplification of the description, it is assumed thata feature-point feature-quantity is one type of feature quantity(vector), and the number of feature-point feature-quantity values(types) of feature points stored in the feature-point feature-quantitystorage section 13 (FIG. 1) is assumed to be K.

The total-feature-quantity generating section 23 calculates correlationvalues of feature-point feature-quantity values of each of featurepoints of the generation image to an observation value by sequentiallyemploying K feature-point feature-quantity values (96-dimensionalvectors) of the model image stored in the feature-point feature-quantitystorage section 13 as the observation value.

The total-feature-quantity generating section 23 detects a maximum valueof the correlation values of the feature-point feature-quantity valuesof each of the feature points of the generation image to the observationvalue, and outputs a K-dimensional vector, having a total of Kfeature-point feature-quantity correlation values obtained from Kfeature-point feature-quantity values of the model image as components,as a total feature quantity of the generation image.

When the feature-point feature-quantity is 6 types of feature quantities(96-dimensional vectors) as described above, K feature-pointfeature-quantity correlation values are produced for every type and a6×K-dimensional vector having a total of 6×K feature-pointfeature-quantity correlation values for 6 types as components isregarded as a total feature quantity of the generation image.

A value proportional to an inner product of a vector as a feature-pointfeature-quantity value of the model image and a vector as afeature-point feature-quantity value of a feature point of thegeneration image can be adopted as a correlation value of thefeature-point feature-quantity value of the feature point of thegeneration image to the feature-point feature-quantity value(observation value) of the model image.

Here, a feature-point feature-quantity correlation value as a totalfeature quantity represents an extent in which a feature-pointfeature-quantity present in the generation image is similar to afeature-point feature-quantity value present in the model image.

A method of performing identification using a correlation value of afeature-point feature-quantity value of a model image and afeature-point feature-quantity value of a generation image is disclosedin Japanese Unexamined Patent Application Publication No. 2007-128195.According to the method disclosed in Japanese Unexamined PatentApplication Publication No. 2007-128195, only feature-pointfeature-quantity values of feature points close to a position of afeature point corresponding to an observation value of the model imageamong feature points on the generation image are used as an object,correlation values with the observation value of the model image areproduced, and a maximum value of the correlation values is adopted as atotal feature quantity.

The total feature quantity is not limited to a feature-pointfeature-quantity value histogram or a feature-point feature-quantitycorrelation value.

FIG. 11 is a flowchart illustrating a total-feature-quantity generatingprocess in which the total-feature-quantity generating section 23produces a feature-point feature-quantity value histogram as a totalfeature quantity of a generation image.

In step S31, the total-feature-quantity generating section 23 selectsone generation image, which has not yet been selected as an observationimage among generation images of feature-point feature-quantitiessupplied from the feature-point feature-quantity extracting section 22(FIG. 1), as the observation image. Then, the process proceeds to stepS32.

In step S32, the total-feature-quantity generating section 23 canproduce a histogram of feature-point feature-quantity values of theobservation image in which feature-point feature-quantity values of amodel image (hereinafter, also referred to as model feature-quantityvalues) stored in the feature-point feature-quantity storage section 13(FIG. 1) are ranked, as a total feature quantity of the observationimage, and supplies the total feature quantity to the identifiergenerating section 24.

The process proceeds from step S32 to step S33, and thetotal-feature-quantity generating section 23 determines whether or nottotal feature quantities of all generation images of feature-pointfeature-quantities supplied from the feature-point feature-quantityextracting section 22 have been produced.

When it has been determined that the total feature quantities of all thegeneration images of the feature-point feature-quantities supplied fromthe feature-point feature-quantity extracting section 22 have not yetbeen produced in step S33, the process returns to step S31. In thefollowing, the same process is repeated.

When it has been determined that the total feature quantities for allthe generation images of the feature-point feature-quantities suppliedfrom the feature-point feature-quantity extracting section 22 have beenproduced in step S33, the total-feature-quantity generating process isterminated.

FIG. 12 is a flowchart illustrating a total-feature-quantity generatingprocess in which the total-feature-quantity generating section 23produces a feature-point feature-quantity correlation value as a totalfeature quantity of a generation image.

In step S41, the total-feature-quantity generating section 23 selectsone generation image, which has not yet been selected as an observationimage among generation images of feature-point feature-quantitiessupplied from the feature-point feature-quantity extracting section 22(FIG. 1), as the observation image. Then, the process proceeds to stepS42.

In step S42, the total-feature-quantity generating section 23 canproduce a maximum value of correlation values with each feature-pointfeature-quantity value of feature points of the generation image to eachmodel feature-quantity value stored in the feature-pointfeature-quantity storage section 13 (FIG. 1) as a feature-pointfeature-quantity correlation value. The total-feature-quantitygenerating section 23 supplies the identifier generating section 24 withthe feature-point feature-quantity correlation value as a total featurequantity of the observation image. Then, the process proceeds from stepS42 to step S43.

In step S43, the total-feature-quantity generating section 23 determineswhether total feature quantities for all generation images offeature-point feature-quantities supplied from the feature-pointfeature-quantity extracting section 22 have been produced.

When it has been determined that the total feature quantities for allthe generation images of the feature-point feature-quantities suppliedfrom the feature-point feature-quantity extracting section 22 have notyet been produced in step S43, the process returns to step S41. In thefollowing, the same process is repeated.

When it has been determined that the total feature quantities for allthe generation images of the feature-point feature-quantities suppliedfrom the feature-point feature-quantity extracting section 22 have beenproduced in step S43, the total-feature-quantity generating process isterminated.

Here, a component of a vector as a total feature quantity produced inthe total-feature-quantity generating section 23 (for example, thefrequency of the feature-point feature-quantity value histogram or thefeature-point feature-quantity correlation value described above) is adimensional feature quantity described with reference to FIG. 1.

Process of Identifier Generating Section 24

The outline of a process of the identifier generating section 24 of FIG.1 will be described with reference to FIG. 13.

For example, the identifier generating section 24 selects (a dimensionof) a dimensional feature quantity for use in identification amongdimensional feature quantities constituting a total feature quantityfrom the total-feature-quantity generating section 23 according to aboosting algorithm, and generates an identifier for performingidentification using the dimensional feature quantity.

That is, the identifier generating section 24 generates an identifierfor performing identification using the dimensional feature quantitywhich decreases an error value indicating a level at which a positiveimage and a negative image are erroneously identified among a pluralityof dimensional feature quantities (vector components) constituting atotal feature quantity from the total-feature-quantity generatingsection 23 (FIG. 1), and generates dimensional information indicatingthe dimension of the dimensional feature quantity which decreases theerror value.

Specifically, it is assumed that N images exist as generation images,and the total-feature-quantity generating section 23 obtains vectors astotal feature quantities x₁, x₂, . . . , x_(N) of N samples of Ngeneration images as illustrated in FIG. 13.

As illustrated in FIG. 13, it is assumed that the total feature quantityx_(i) (i=1, 2, . . . , N) is an M-dimensional vector having M components(dimensional feature quantities) x_(i,1), x_(i,2), . . . , x_(i,M).

As described with reference to FIG. 1, a true label is supplied to theidentifier generating section 24. The true label of an i^(th) samplei^(th) generation image) is denoted by y_(i). For example, the truelabel y_(i) becomes +1 when the i^(th) generation image is a positiveimage, and the true label y_(i) becomes −1 when the i^(th) generationimage is a negative image.

The identifier generated by the identifier generating section 24 is afunction for performing identification using a dimensional featurequantity x_(i,d) which decreases an error value indicating an extent towhich a positive image and a negative image are erroneously identifiedamong the M dimensional feature quantities x_(i,1) to x_(i,M)constituting the total feature quantity x_(i), and includes a pluralityof weak learners h_(t,d)(x_(i,d)).

Here, the suffix t of the weak learners h_(t,d)(x_(i,d)) is a variablefor counting the number of weak learners h_(t,d)(x_(i,d)), and theidentifier includes T weak learners h_(1,d)(x_(i,d)), h_(2,d) (x_(i,d)),. . . , h_(T,d) (x_(i,d)).

For example, the number of weak learners h_(t,d)(x_(i,d), T, isempirically set, or is set to a value equal to or less than M so that anidentification ratio of identification by the identifier is equal to orgreater than a certain level value.

The weak learner h_(t,d)(x_(i,d)) is a function of outputting anidentification result indicating whether a generation image is apositive or negative image using a d^(th) dimensional feature quantity(a d^(th) component of a vector as the total feature quantity x_(i))x_(i,d) of the total feature quantity x_(i) of the generation image asan input. For example, the weak learner outputs +1 when theidentification result indicates the positive image, and outputs −1 whenthe identification result indicates the negative image.

When an error value of the identification result of the weak learnerh_(t,d)(x_(i,d)) is denoted by ε_(t,d), the identifier generatingsection 24 determines the weak learner h_(t,d)(x_(i,d)) to decrease theerror value ε_(t,d).

Here, for simplification of the description, it is assumed that the weaklearner h_(t,d)(x_(i,d)) adopts, for example, a function of outputting+1 indicating the identification result of the positive image when thed^(th) dimensional feature quantity x_(i,d) as a parameter is equal toor greater than a predetermined threshold value, and outputting −1indicating the identification result of the negative image when thed^(th) dimensional feature quantity x_(i,d) is less than thepredetermined threshold value.

In this case, when the weak learner h_(t,d)(x_(i,d)) is determined todecrease the error value ε_(t,d), it means that the threshold value ofthe weak learner h_(t,d)(x_(i,d)) is determined. The threshold value ofthe weak learner h_(t,d)(x_(i,d)) is determined to be equal to orgreater than a minimum value and equal to or less than a maximum valueof N d^(th) dimensional feature quantities x_(1,d), x_(2,d), . . . ,x_(N,d) which can be parameters.

The identifier generating section 24 determines each of the weaklearners h_(t,1)(x_(i,1)), h_(t,2)(x_(i,2)), . . . , h_(t,M)(x_(i,M)) todecrease each of the error values ε_(t,i), ε_(t,2), . . . , ε_(t,M), andproduces a dimension (hereinafter, also referred to as a minimum errordimension) d(t) for obtaining a minimum value of the error valuesε_(t,1) to ε_(t,M).

The identifier generating section 24 produces a weight D_(t)(i) ofmaking the error value ε_(t,d) affect an error of the identificationresult of the generation image for every generation image according towhether or not the identification result of an i^(th) generation imageby the weak learner h_(t,d)(x_(i,d)) matches the true label y_(i), thatis, whether the expression h_(t,d)(x_(i,d))=y_(i) is established orwhether the expression h_(t,d)(x_(i,d))≠y_(i) is established.

Here, the error value ε_(t,d) can be produced by adding the weightD_(t)(i) of a generation image for which the identification result bythe weak learner h_(t,d)(x_(i,d)) is erroneous among N generationimages.

The identifier generating section 24 generates an identifier H(x)including T weak learners h_(1,d)(x_(i,d)), h_(2,d)(x_(i,d)), . . . ,h_(T,d) (x_(i,d)) and dimensional information indicating minimum errordimensions d(1), d(2), . . . , d(T) by determining the weak learnerh_(t,d)(x_(i,d)) to decrease the error value ε_(t,d), producing adimension (minimum error dimension) d(t) capable of obtaining theminimum value of the error values ε_(t,1) to ε_(t,M) of theidentification result of the generation image by the weak learnerh_(t,d)(x_(i,d)), producing the weight D_(t)(i) to be used to calculatethe error value ε_(t,d), and repeating the above process T times.

An identifier generating process in which the identifier generatingsection 24 of FIG. 1 generates an identifier and dimensional informationwill be described with reference to FIG. 14.

In step S61, the identifier generating section 24 sets initial valuesD₁(1), D₁(2), . . . , D₁(N) of the weight D_(t)(i) of making the errorvalue ε_(t,d), representing an identification error level of the weaklearner h_(t,d)(x_(i,d)), affect an error of the identification resultof the i^(th) generation image, for example, according to Expression(14). The process proceeds to step S62.

$\begin{matrix}{{D_{t}(i)} = \frac{1}{N}} & {{Expression}\mspace{14mu} 14}\end{matrix}$

In step S62, the identifier generating section 24 initializes a variablet for counting the number of weak learners h_(t,d)(x_(i,d)) constitutingthe identifier H(x) to 1. The process proceeds to step S63.

In step S63, the identifier generating section 24 determines (athreshold value TH_(t,d) of) the weak learner h_(t,d)(x_(i,d)) so thatthe error value ε_(t,d) produced using the weight D_(t)(i) can beminimized for dimensions d=1, 2, . . . , M of the total feature quantityx_(i). The process proceeds to step S64.

Here, in step S63, the identifier generating section 24 determines thethreshold value TH_(t,d) of the weak learner h_(t,d)(x_(i,d)) so thatthe error value ε_(t,d) calculated according to, for example, Expression(15), can be minimized.

$\begin{matrix}{ɛ_{t,d} = {\sum\limits_{i = 1}^{N}\; {{D_{t}(i)}\left\lbrack {y_{i} \neq {h_{t,d}\left( x_{i,d} \right)}} \right\rbrack}}} & {{Expression}\mspace{14mu} 15}\end{matrix}$

In Expression (15), [y_(i)≠h_(t,d)(x_(i,d))] is an indicator function,which becomes 1 when the expression [y_(i)≠h_(t,d)(x_(i,d))] isestablished, and becomes 0 when the expression [y_(i)≠h_(t,d)(x_(i,d))]is not established.

According to Expression (15), the error value ε_(t,d) can be produced byadding only the weight D_(t)(i) of a generation image for which theidentification result by the weak learner h_(t,d)(x_(i,d)) (a generationimage for which the expression y_(i)≠h_(t,d)(x_(i,d)) is established) iserroneous among N generation images.

In step S64, the identifier generating section 24 produces a minimumvalue ε_(t) of the error values ε_(t,1), ε_(t,2), . . . , ε_(t,M)calculated according to Expression (15) using the weak learnerh_(t,d)(x_(i,d)) determined for each of the dimensions d=1, 2, . . . , Min the previous step S63. The identifier generating section 24 producesa dimension (minimum error dimension) d(t) (the integer value in therange of 1 to M) in which the minimum value ε_(t) of the error valuesε_(t,1), ε_(t,2), . . . , ε_(t,M) is obtained. The process proceeds fromstep S64 to step S65.

Here, the minimum error dimension d(t) is a dimension of a dimensionalfeature quantity for use in identification by the identifier H(x) amongdimensional feature quantities constituting a total feature quantity.Therefore, the dimensional feature quantity of the minimum errordimension d(t) among the dimensional feature quantities constituting thetotal feature quantity is selected for identification by the identifierH(x) for use in identification.

Assuming that the minimum value ε_(t) of the error values ε_(t,1),ε_(t,2), . . . , ε_(t,M) is the minimum error value ε_(t), the weaklearner h_(t,d(t))(x_(i,d(t))) becomes a t^(th) weak learnerconstituting the identifier H(x).

In step S65, the identifier generating section 24 produces a reliabilitylevel α_(t) indicating the identification reliability of a generationimage by the t^(th) weak learner h_(t,d(t))(x_(i,d(t))) constituting theidentifier H(x) using the minimum error value ε_(t) produced in theprevious step S64 according to Expression (16). The process proceeds tostep S66.

$\begin{matrix}{\alpha_{t} = {\frac{1}{2}{\ln \left( \frac{1 - ɛ_{t}}{ɛ_{t}} \right)}}} & {{Expression}\mspace{14mu} 16}\end{matrix}$

Here, according to Expression (16), a value of the reliability levelα_(t) is as small (or large) as the minimum error value ε_(t) is large(or small).

In step S66, the identifier generating section 24 updates the weightD_(t)(i) to a weight D_(t+i)(i) according to Expression (17). Theprocess proceeds to step S67.

$\begin{matrix}\begin{matrix}{{D_{t + 1}(i)} = {\frac{D_{t}(i)}{Z_{t}} \times \left\{ \begin{matrix}^{- \alpha_{t}} & {{{if}\mspace{14mu} {h_{t,{d{(t)}}}\left( x_{i,{d{(t)}}} \right)}} = y_{i}} \\^{\alpha_{t}} & {{{if}\mspace{14mu} {h_{t,{d{(t)}}}\left( x_{i,{d{(t)}}} \right)}} \neq y_{i}}\end{matrix} \right.}} \\{= {\frac{D_{t}(i)}{Z_{t}} \times ^{{- \alpha_{t}}y_{i}{h_{t,{d{(t)}}}{(x_{i,{d{(t)}}})}}}}}\end{matrix} & {{Expression}\mspace{14mu} 17}\end{matrix}$

Here, a coefficient Z_(t) in Expression (17) is a coefficient fornormalizing the weight D_(t+i)(i), and is expressed by Expression (18).

$\begin{matrix}{Z_{t} = {\sum\limits_{i = 1}^{N}\; {{D_{t}(i)}^{{- \alpha_{t}}y_{i}{h_{t,{d{(t)}}}{(x_{i,{d{(t)}}})}}}}}} & {{Expression}\mspace{14mu} 18}\end{matrix}$

For the i^(th) generation image for which the identification result bythe weak learner h_(t,d(t))(x_(i,d(t))) is correct, that is, thegeneration image for which the identification result matches the truelabel y_(i), the weight D_(t)(i) is updated to a weight D_(t+i)(i)having a smaller value according to Expression (17). Consequently, inthe next step S63, the error value ε_(t,d) calculated using the weightD_(t)(i) is decreased.

On the other hand, for the i^(th) generation image for which theidentification result by the weak learner h_(t,d(t))(x_(i,d(t))) iserroneous, that is, the generation image for which the identificationresult does not match the true label y_(i), the weight D_(t)(i) isupdated to a weight D_(t+1)(i) having a larger value. Consequently, inthe next step S63, the error value ε_(t,d) calculated using the weightD_(t)(i) is increased.

In step S67, the identifier generating section 24 determines whether ornot the variable t is the same as the number of weak learnersh_(t,d)(x_(i,d)) (hereinafter, also referred to as the number of weaklearners), T.

When it has been determined that the variable t is not the same as thenumber of weak learners T in step S67, the process proceeds to step S68.The identifier generating section 24 increments the variable t by 1. Theprocess returns from step S68 to step S63. In the following, the sameprocess is repeated.

When it has been determined that the variable t is the same as thenumber of weak learners T, that is, when T weak learnersh_(1,d(1))(x_(i,d(1))), h_(2,d(2))(x_(i,d(2))), . . . ,h_(T,d(T))(x_(i,d(T))) constituting the identifier H(x) and T minimumerror dimensions d(1), d(2), . . . , d(T) have been generated in stepS67, the process proceeds to step S69. The identifier generating section24 outputs the T weak learners h_(1,d(1))(x_(i,d(1))),h_(2,d(2))(x_(i,d(2))), . . . , h_(T,d(T))(x_(i,d(T))) and T reliabilitylevels α₁, α₂, . . . , α_(T) as (parameters defining) the identifierH(x).

The identifier generating section 24 outputs the T minimum errordimensions d(1), d(2), . . . , d(T) as dimensional information in stepS69, and then the identifier generating process is terminated.

By boosting-based statistical learning as described above, theidentifier generating section 24 produces the identifier H(x) forperforming identification using dimensions (minimum error dimensions)d(1) to d(T) indicating T dimensional feature quantities, which are morevalid to identify an identification object, and dimensional featurequantities of the minimum error dimensions d(t).

Description of Learning Process of Learning Device

A process (learning process) to be executed by the learning device ofFIG. 1 will be described with reference to FIG. 15.

In the learning device, a model image is supplied to the feature-pointextracting section 11, and a generation image is supplied to thefeature-point extracting section 21. A true label is supplied to theidentifier generating section 24.

In the learning device, in step S81, the feature-point extractingsection 11 extracts feature points from a model image supplied thereto,and supplies the feature-point feature-quantity extracting section 12with the feature points and the model image.

In step S81, the feature-point extracting section 21 extracts thefeature points from the generation image supplied thereto and suppliesthe feature-point feature-quantity extracting section 22 with thegeneration image and the feature points. The process proceeds to stepS82.

In step S82, the feature-point feature-quantity extracting section 12extracts feature-point feature-quantities of the feature points suppliedby the feature-point extracting section 11 from the model image suppliedby the feature-point extracting section 11 (performs the feature-pointfeature-quantity extracting process of FIG. 9), and supplies thefeature-point feature-quantity storage section 13 with the extractedfeature-point feature-quantities, so that the feature-pointfeature-quantities are stored in the feature-point feature-quantitystorage section 13.

Furthermore, in step S82, the feature-point feature-quantity extractingsection 22 extracts feature-point feature-quantities of the featurepoints supplied by the feature-point extracting section 21 from thegeneration image supplied by the feature-point extracting section 21,and supplies the total-feature-quantity generating section 23 with theextracted feature-point feature-quantities. The process proceeds to stepS83.

In step S83, the total-feature-quantity generating section 23 produces atotal feature quantity indicating a feature of the entire generationimage from the feature-point feature-quantities of the generation imagefrom the feature-point feature-quantity extracting section 22 on thebasis of the feature-point feature-quantities of the model image storedin the feature-point feature-quantity storage section 13 (performs atotal-feature-quantity generating process of FIG. 11 or 12).Furthermore, in step S83, the total-feature-quantity generating section23 supplies the identifier generating section 24 with the total featurequantity of the generation image. The process proceeds to step S84.

In step S84, the identifier generating section 24 generates and outputsan identifier and dimensional information (performs the identifiergenerating process of FIG. 14) by boosting-based statistical learningusing a total feature quantity of the generation image from thetotal-feature-quantity generating section 23 and a true label of thegeneration image. Then, the learning process is terminated.

Identifiers for identifying a plurality of different identificationobjects and dimensional information are generated by preparing learningimages (model images and generation images) and true labels for everydifferent identification object and performing the learning process ofFIG. 15.

Configuration Example of Identification Device According to Embodiment

FIG. 16 is a block diagram illustrating an identification deviceaccording to an embodiment of the present invention.

In FIG. 16, the identification device identifies whether a subjectviewed in a processing object image is a predetermined identificationobject using an identifier H(x) and minimum error dimensions d(1) tod(T) as dimensional information obtained by the learning device of FIG.1.

That is, the identification device includes a feature-pointfeature-quantity storage section 61, a dimensional-information storagesection 62, an identifier storage section 63, a feature-point extractingsection 71, a feature-point feature-quantity extracting section 72, adimensional-feature-quantity generating section 73, and anidentification section 74.

The feature-point feature-quantity storage section 61 storesfeature-point feature-quantities of a model image obtained by thefeature-point feature-quantity extracting section 12 of the learningdevice of FIG. 1 for a predetermined identification object (which arethe same as feature-point feature-quantities stored in the feature-pointfeature-quantity storage section 13).

The dimensional-information storage section 62 stores minimum errordimensions d(1) to d(T) as dimensional information obtained by theidentifier generating section 24 of the learning device of FIG. 1 forthe predetermined identification object.

The identifier storage section 63 stores T weak learnersh_(1,d(1))(x_(i,d(1))), h_(2,d(2))(x_(i,d(2))), . . . ,h_(T,d(T))(x_(i,d(T))) as an identifier H(x) obtained by the identifiergenerating section 24 of the learning device of FIG. 1 for thepredetermined identification object and T reliability levels α₁, α₂, . .. , α_(T).

A processing object image of an object for identifying whether a subjectviewed in an image is the predetermined identification object issupplied to the feature-point extracting section 71. Like thefeature-point extracting section 11 of FIG. 1, the feature-pointextracting section 71 extracts feature points from the processing objectimage supplied thereto, and supplies the feature-point feature-quantityextracting section 72 with the feature points and the processing objectimage.

The feature-point feature-quantity extracting section 72 extractsfeature-point feature-quantities of the same feature points supplied bythe feature-point extracting section 71 from the processing object imagesupplied by the feature-point extracting section 71, and supplies thedimensional-feature-quantity generating section 73 with thefeature-point feature-quantities.

Like the total-feature-quantity generating section 23 of the learningdevice of FIG. 1, the dimensional-feature-quantity generating section 73produces dimensional feature quantities constituting a total featurequantity of the processing object image from the feature-pointfeature-quantities of the processing object image from the feature-pointfeature-quantity extracting section 72 on the basis of feature-pointfeature-quantities of the model image stored in the feature-pointfeature-quantity storage section 61.

In this regard, the dimensional-feature-quantity generating section 73does not produce all M (M-dimensional) dimensional feature quantitiesconstituting the total feature quantity of the processing object image,but selectively produces dimensional feature quantities of minimum errordimensions d(1) to d(T) as dimensional information stored in thedimensional-information storage section 62 among the M dimensionalfeature quantities.

The dimensional-feature-quantity generating section 73 may produce onlythe dimensional feature quantities of minimum error dimensions d(1) tod(T) in the total feature quantity of the processing object image fromthe start, or may produce the total feature quantity of the processingobject image and extract the dimensional feature quantities of theminimum error dimensions d(1) to d(T) from the total feature quantities.

Here, for example, a vector having M dimensional feature quantities ascomponents is denoted by x′ as a total feature quantity of theprocessing object image constituted by the M dimensional featurequantities. An m^(th) item of the M dimensional feature quantities ofthe total feature quantity x′ of the processing object image is denotedby x′_(m).

In this case, dimensional feature quantities of minimum error dimensionsd(1) to d(T) among the M dimensional feature quantities of the totalfeature quantity x′ of the processing object image are denoted byx′_(d(1)), x′_(d(2)), . . . , x′_(d(T)).

The dimensional-feature-quantity generating section 73 selects(selectively produces) the T dimensional feature quantities x′_(d(1)),x′_(d(2)), . . . , x′_(d(T)) of the minimum error dimensions d(1) tod(T) among the M dimensional feature quantities of the total featurequantity x′ of the processing object image, and supplies theidentification section 74 with the T dimensional feature quantities.

The identification section 74 identifies whether or not a subject viewedin the processing object image is a predetermined identification objectby providing the identifier H(x′) stored in the identifier storagesection 63 with the dimensional feature quantities x′_(d(1)) tox′_(d(T)) of the minimum error dimensions d(1) to d(T) of the processingobject image from the dimensional-feature-quantity generating section 73as an input x′, and outputs the identification result.

That is, the identification section 74 calculates the function H(x′) ofExpression (19) as the identifier H(x′) using T weak learnersh_(1,d(1))(x′_(d(1))), h_(2,d(2))(x′_(d(2))), . . . ,h_(T,d(T))(x′_(d(T))) as the identifier H(x′) stored in the identifierstorage section 63 and T reliability levels α₁, α₂, . . . , α_(T).

$\begin{matrix}{{H\left( x^{\prime} \right)} = {{sign}\left( {\sum\limits_{t = 1}^{T}\; {\alpha_{t}{h_{t,{d{(t)}}}\left( x_{d{(t)}}^{\prime} \right)}}} \right)}} & {{Expression}\mspace{14mu} 19}\end{matrix}$

Here, in Expression (19), for example, sign( ) is a function ofoutputting +1 when the sign within the parentheses ( ) is positive, andoutputting −1 when the sign within the parentheses ( ) is negative.Therefore, a value of the function H(x′) of Expression (19) becomes +1or −1.

When the value of the function H(x′) of Expression (19) is +1, theidentification result indicates that the subject viewed in theprocessing object image is the predetermined identification object. Whenthe value of the function H(x′) of Expression (19) is −1, theidentification result indicates that the subject viewed in theprocessing object image is not the predetermined identification object.

Description of Identification Process of Identification Device

A process (identification process) to be executed by the identificationdevice of FIG. 16 will be described with reference to FIG. 17.

In the identification device, a processing object image is supplied tothe feature-point extracting section 71.

In step S91, the feature-point extracting section 71 extracts featurepoints from the processing object image supplied thereto, and suppliesthe feature-point feature-quantity extracting section 72 with thefeature points and the processing object image. The process proceeds tostep S92.

In step S92, the feature-point feature-quantity extracting section 72extracts feature-point feature-quantities of the same feature pointssupplied by the feature-point extracting section 71 from the processingobject image supplied by the feature-point extracting section 71, andsupplies the dimensional-feature-quantity generating section 73 with thefeature-point feature-quantities. The process proceeds to step S93.

In step S93, the dimensional-feature-quantity generating section 73produces dimensional feature quantities x′_(d(1)) to x′_(d(T)) ofminimum error dimensions d(1) to d(T) as dimensional information storedin the dimensional-information storage section 62 among dimensionalfeature quantities constituting a total feature quantity of theprocessing object image from the feature-point feature-quantities of theprocessing object image supplied by the feature-point feature-quantityextracting section 72 on the basis of feature-point feature-quantitiesof the model image stored in the feature-point feature-quantity storagesection 61.

The dimensional-feature-quantity generating section 73 supplies theidentification section 74 with the dimensional feature quantitiesx′_(d(1)) to x′_(d(T)) of the minimum error dimensions d(1) to d(T). Theprocess proceeds from step S93 to step S94.

In step S94, the identification section 74 identifies whether or not asubject viewed in the processing object image is a predeterminedidentification object by applying the dimensional feature quantitiesx′_(d(1)) to x′_(d(T)) of the minimum error dimensions d(1) to d(T) ofthe processing object image from the dimensional-feature-quantitygenerating section 73 as an input x′ to the identifier H(x′) expressedby Expression (19) stored in the identifier storage section 63, andoutputs the identification result. The identification process isterminated.

Feature-point feature-quantities produced by the feature-pointfeature-quantity extracting section 72 of the identification device ofFIG. 16 like the feature-point feature-quantity extracting section 12 ofFIG. 1 have high discrimination and invariance as described withreference to FIG. 8. The identification device can performidentification with high discrimination and invariance by identifying aprocessing object image using dimensional feature quantities generatedfrom the above-described feature-point feature-quantities.

Description of Computer According to Embodiment of Present Invention

The series of processes described above may be executed by hardware orsoftware. When the series of processes is executed by software, aprogram constituting the software is installed in a general-purposecomputer or the like.

FIG. 18 illustrates a configuration example of a computer where aprogram for executing the series of processes described above isinstalled.

The program may be recorded in advance on a hard disk 105 or a Read OnlyMemory (ROM) 103 built in the computer and serving as a recordingmedium.

Alternatively, the program may be stored (recorded) on a removablerecording medium 111. The removable recording medium 111 may be providedas so-called package software. For example, a flexible disk, a CompactDisc-Read Only Memory (CD-ROM), a Magneto Optical (MO) disk, a DigitalVersatile Disk (DVD), a magnetic disk, or a semiconductor memory existsas the removable recording medium 111.

The program may be installed from the above-described removablerecording medium 111 to the computer. The program may also be installedin the built-in hard disk 105 by downloading the program to a computervia a communication network or a broadcasting network. That is, it ispossible to transmit the program wirelessly from a download site to thecomputer via an artificial satellite for digital satellite broadcastingor to transmit the program to the computer using a wired link via anetwork such as a Local Area Network (LAN) or the Internet.

The computer has a central processing section (CPU) 102 built therein.An input/output interface 110 is connected to the CPU 102 via a bus 101.

When a user inputs an instruction by manipulating an input section 107through the input/output interface 110, the CPU 102 executes a programstored in the Read Only Memory (ROM) 103 according to the instruction.Alternatively, the CPU 102 loads a program stored in the hard disk 105in a Random Access Memory (RAM) 104 and executes the program loaded inthe RAM 104.

Thus, the CPU 102 executes a process in accordance with the flowchartdescribed above or a process to be executed by the configuration of theabove-described block diagram. Then, the CPU 102, for example, outputsthe processing result from an output section 106 via the input/outputinterface 110 as necessary, transmits the processing result from acommunication section 108, or records the processing result in the harddisk 105.

The input section 107 may include a keyboard, a mouse, and/or amicrophone. The output section 106 may include a Liquid Crystal Display(LCD) section and/or a speaker.

In the present specification, the process to be executed by the computeraccording to the program is not necessarily executed chronologicallyaccording to the sequence noted on the flowchart. That is, the processto be executed by the computer according to the program may include aprocess to be executed in parallel or separately (for example, parallelprocessing or object processing).

The program may be processed by a single computer or may be processed bya plurality of computers in a distributed processing manner.Furthermore, the program may be transferred to and executed by a remotecomputer.

The present application contains subject matter related to thatdisclosed in Japanese Priority Patent Application JP 2009-036500 filedin the Japan Patent Office on Feb. 19, 2009, the entire content of whichis hereby incorporated by reference.

It should be understood by those skilled in the art that variousmodifications, combinations, sub-combinations and alterations may occurdepending on design requirements and other factors insofar as they arewithin the scope of the appended claims or the equivalents thereof.

1. A learning device comprising: feature-point extracting means forextracting feature points as characteristic points from one of aplurality of generation images including a positive image in which apredetermined identification object is viewed and a negative image inwhich no identification object is viewed for use in learning to generatean identifier for identifying whether or not a subject viewed in animage is the identification object; feature-point feature-quantityextracting means for extracting feature-point feature-quantitiesrepresenting features of the feature points of the generation image;total-feature-quantity generating means for generating a total featurequantity represented by a multi-dimensional vector from thefeature-point feature-quantities of the generation image, wherein thetotal feature quantity represents a feature of the entire generationimage; and identifier generating means for generating the identifierusing the total feature quantity of the generation image and a truelabel indicating whether or not the generation image is a positive imageor a negative image; wherein the feature-point feature-quantityextracting means divides a feature-point area into a plurality of smallareas by separating the feature-point area as an area having a featurepoint as a center in an angular direction and a distance direction onthe basis of the feature point in each of a plurality of response imagesobtained by filtering the generation image by a plurality of filtershaving different characteristics, produces a statistical quantity ofpixel values of a small area for each of the plurality of small areas,and sets the statistical quantity of each of the plurality of smallareas obtained from each of the plurality of response images for thefeature point as a feature-point feature-quantity of the feature point,and wherein the identifier generating means generates the identifier forperforming identification using a dimensional feature quantity todecrease an error value representing an identification error level ofthe positive and negative images among a plurality of dimensionalfeature quantities which are components of the multi-dimensional vectoras the total feature quantity, and generates dimensional informationrepresenting a dimension of the dimensional feature quantity to decreasethe error value.
 2. The learning device according to claim 1, whereinthe feature-point feature-quantity extracting means includes: filtermeans for filtering the generation image by each of derivatives based onGaussian functions of a plurality of scales σ, a plurality of angle θdirections, and a plurality of differentiations c and outputting theplurality of response images; and feature-point feature-quantitycalculating means for dividing the feature-point area into the pluralityof small areas by separating the feature-point area as a circular areahaving the feature point as the center and having a fixed radius on thebasis of the feature point in the response image obtained by filteringin a derivative based on the same scale σ, the same angle θ direction,and the same number of differentiations c, producing an average value ofpixel values of the small area as the statistical quantity for each ofthe plurality of small areas, and producing types of feature quantitiescorresponding to a number of combinations of the plurality of scales σand the plurality of differentiations c as feature-pointfeature-quantities of the feature point using a vector having acomponent of the average value of the pixel values of the small areaproduced from the response image obtained by filtering in each ofderivatives based on a Gaussian function of the same scale σ, aplurality of angle θ directions, and the same number of differentiationsc as one type of feature quantity.
 3. The learning device according toclaim 2, further comprising: second feature-point extracting means forextracting the feature points from a model image as the positive image;and second feature-point feature-quantity extracting means forextracting feature-point feature-quantities of the feature points of themodel image, wherein the total-feature-quantity generating meansproduces a histogram of feature-point feature-quantity values of thegeneration image in which feature-point feature-quantity values asvalues of the feature-point feature-quantities of the model image areranked, or a correlation value of the feature-point feature-quantityvalues of the generation image to the feature-point feature-quantityvalues of the model image, as the total feature quantity.
 4. Thelearning device according to claim 2, wherein the identifier includes aplurality of weak learners, wherein the identifier generating meansdetermines a weak learner for outputting an identification resultindicating whether the generation image is the positive image or thenegative image so that the error value is decreased using the totalfeature quantity of the generation image as an input, produces a minimumerror dimension as a dimension of the dimensional feature quantity inwhich a minimum value of the error value of the weak learner is obtainedamong the plurality of dimensional feature quantities constituting thetotal feature quantity, and repeats a process of producing a weight ofmaking the error value affect an error of an identification result ofthe generation image according to whether the identification result ofthe generation image by the weak learner matches the true label forevery generation image a predetermined number of times, therebygenerating the identifier including a predetermined number of weaklearners corresponding to the predetermined number of times and thedimensional information representing the minimum error dimensioncorresponding to the predetermined number of weak learners, and whereinthe error value is produced by adding the weight of the generation imageof which the identification result is erroneous among the plurality ofgeneration images.
 5. The learning device according to claim 2, whereinthe feature-point extracting means extracts a corner point as thefeature point.
 6. A learning method comprising the steps of: extractingfeature points as characteristic points from one of a plurality ofgeneration images including a positive image in which a predeterminedidentification object is viewed and a negative image in which noidentification object is viewed for use in learning to generate anidentifier for identifying whether or not a subject viewed in an imageis the identification object; extracting feature-pointfeature-quantities representing features of the feature points of thegeneration image; generating a total feature quantity represented by amulti-dimensional vector from the feature-point feature-quantities ofthe generation image, wherein the total feature quantity represents afeature of the entire generation image; and generating the identifierusing the total feature quantity of the generation image and a truelabel indicating whether or not the generation image is a positive imageor a negative image; wherein the extracting step is performed bydividing a feature-point area into a plurality of small areas byseparating the feature-point area as an area having a feature point as acenter in an angular direction and a distance direction on the basis ofthe feature point in each of a plurality of response images obtained byfiltering the generation image by a plurality of filters havingdifferent characteristics, producing a statistical quantity of pixelvalues of a small area for each of the plurality of small areas, andsetting the statistical quantity of each of the plurality of small areasobtained from each of the plurality of response images for the featurepoint as a feature-point feature-quantity of the feature point, andwherein the generating step is performed by generating the identifierfor performing identification using a dimensional feature quantity todecrease an error value representing an identification error level ofthe positive and negative images among a plurality of dimensionalfeature quantities which are components of the multi-dimensional vectoras the total feature quantity, and generating dimensional informationrepresenting a dimension of the dimensional feature quantity to decreasethe error value.
 7. A program for making a computer function as:feature-point extracting means for extracting feature points ascharacteristic points from one of a plurality of generation imagesincluding a positive image in which a predetermined identificationobject is viewed and a negative image in which no identification objectis viewed for use in learning to generate an identifier for identifyingwhether or not a subject viewed in an image is the identificationobject; feature-point feature-quantity extracting means for extractingfeature-point feature-quantities representing features of the featurepoints of the generation image; total-feature-quantity generating meansfor producing a total feature quantity represented by amulti-dimensional vector from the feature-point feature-quantities ofthe generation image, wherein the total feature quantity represents afeature of the entire generation image; and identifier generating meansfor generating the identifier using the total feature quantity of thegeneration image and a true label indicating whether or not thegeneration image is a positive image or a negative image; wherein thefeature-point feature-quantity extracting means divides a feature-pointarea into a plurality of small areas by separating the feature-pointarea as an area having a feature point as a center in an angulardirection and a distance direction on the basis of the feature point ineach of a plurality of response images obtained by filtering thegeneration image by a plurality of filters having differentcharacteristics, produces a statistical quantity of pixel values of asmall area for each of the plurality of small areas, and sets thestatistical quantity of each of the plurality of small areas obtainedfrom each of the plurality of response images for the feature point as afeature-point feature-quantity of the feature point, and wherein theidentifier generating means generates the identifier for performingidentification using a dimensional feature quantity to decrease an errorvalue representing an identification error level of the positive andnegative images among a plurality of dimensional feature quantitieswhich are components of the multi-dimensional vector as the totalfeature quantity, and generates dimensional information representing adimension of the dimensional feature quantity to decrease the errorvalue.
 8. An identification device comprising: feature-point extractingmeans for extracting feature points as characteristic points from aprocessing object image of an object used to identify whether or not asubject viewed in the image is a predetermined identification object;feature-point feature-quantity extracting means for extractingfeature-point feature-quantities representing features of the featurepoints; dimensional-feature-quantity generating means for generating adimensional feature quantity of a dimension represented by dimensionalinformation among a plurality of dimensional feature quantities whichare components of a vector as a total feature quantity represented by amulti-dimensional vector from the feature-point feature-quantities ofthe processing object image, wherein the total feature quantityrepresents a feature of the entire processing object image; andidentification means for identifying whether or not the subject viewedin the processing object image is the predetermined identificationobject by inputting the dimensional feature quantity to an identifierfor identifying whether or not the subject viewed in the processingobject image is the predetermined identification object, wherein thefeature-point feature-quantity extracting means divides a feature-pointarea into a plurality of small areas by separating the feature-pointarea as an area having a feature point as a center in an angulardirection and a distance direction on the basis of the feature point ineach of a plurality of response images obtained by filtering theprocessing object image by a plurality of filters having differentcharacteristics, produces a statistical quantity of pixel values of asmall area for each of the plurality of small areas, and sets thestatistical quantity of each of the plurality of small areas obtainedfrom each of the plurality of response images of the processing objectimage for the feature point as a feature-point feature-quantity of thefeature point, wherein the identifier and dimensional information areobtained by extracting feature points from one of a plurality ofgeneration images including a positive image in which a predeterminedidentification object is viewed and a negative image in which noidentification object is viewed for use in learning to generate theidentifier, extracting feature-point feature-quantities representingfeatures of the feature points of the generation image, generating atotal feature quantity of the generation image from the feature-pointfeature-quantities of the generation image, and generating theidentifier using the total feature quantity of the generation image anda true label indicating whether or not the generation image is apositive image or a negative image, wherein the extracting is performedby dividing a feature-point area into a plurality of small areas byseparating the feature-point area as an area having a feature point as acenter in an angular direction and a distance direction on the basis ofthe feature point in each of a plurality of response images obtained byfiltering the generation image by the plurality of filters, producing astatistical quantity of pixel values of a small area for each of theplurality of small areas, and setting the statistical quantity of eachof the plurality of small areas obtained from each of the plurality ofresponse images for the feature point as a feature-pointfeature-quantity of the feature point, and wherein the generating isperformed by generating the identifier for performing identificationusing a dimensional feature quantity to decrease an error valuerepresenting an identification error level of the positive and negativeimages among a plurality of dimensional feature quantities which arecomponents of the multi-dimensional vector as the total featurequantity, and generating dimensional information representing adimension of the dimensional feature quantity to decrease the errorvalue.
 9. The identification device according to claim 8, wherein thefeature-point feature-quantity extracting means includes: filter meansfor filtering the processing object image by each of derivatives basedon Gaussian functions of a plurality of scales σ, a plurality of angle θdirections, and a plurality of differentiations c and outputting theplurality of response images; and feature-point feature-quantitycalculating means for dividing the feature-point area into the pluralityof small areas by separating the feature-point area as a circular areahaving the feature point as the center and having a fixed radius on thebasis of the feature point in the response image obtained by filteringin a derivative based on the same scale σ, the same angle θ direction,and the same number of differentiations c, producing an average value ofpixel values of the small area as the statistical quantity for each ofthe plurality of small areas, and producing types of feature quantitiescorresponding to a number of combinations of the plurality of scales σand the plurality of differentiations c as feature-pointfeature-quantities of the feature point using a vector having acomponent of the average value of the pixel values of the small areaproduced from the response image obtained by filtering in each ofderivatives based on a Gaussian function of the same scale σ, aplurality of angle θ directions, and the same number of differentiationsc as one type of feature quantity.
 10. The identification deviceaccording to claim 9, wherein the total feature quantity of theprocessing object image is a histogram of feature-point feature-quantityvalues of the processing object image in which feature-pointfeature-quantity values as values of the feature-pointfeature-quantities of a model image are ranked, or a correlation valueof the feature-point feature-quantity values of the processing objectimage to the feature-point feature-quantity values of the model image.11. The identification device according to claim 9, wherein thefeature-point extracting means extracts a corner point as the featurepoint.
 12. An identification method comprising the steps of: extractingfeature points as characteristic points from a processing object imageof an object used to identify whether or not a subject viewed in theimage is a predetermined identification object; extracting feature-pointfeature-quantities representing features of the feature points;generating a dimensional feature quantity of a dimension represented bydimensional information among a plurality of dimensional featurequantities which are components of a vector as a total feature quantityrepresented by a multi-dimensional vector from the feature-pointfeature-quantities of the processing object image, wherein the totalfeature quantity represents a feature of the entire processing objectimage; and identifying whether or not the subject viewed in theprocessing object image is the predetermined identification object byinputting the dimensional feature quantity to an identifier foridentifying whether or not the subject viewed in the processing objectimage is the predetermined identification object, wherein thefeature-point feature-quantity extracting step includes dividing afeature-point area into a plurality of small areas by separating thefeature-point area as an area having a feature point as a center in anangular direction and a distance direction on the basis of the featurepoint in each of a plurality of response images obtained by filteringthe processing object image by a plurality of filters having differentcharacteristics, producing a statistical quantity of pixel values of asmall area for each of the plurality of small areas, and setting thestatistical quantity of each of the plurality of small areas obtainedfrom each of the plurality of response images of the processing objectimage for the feature point as a feature-point feature-quantity of thefeature point, wherein the identifier and dimensional information areobtained by extracting feature points from one of a plurality ofgeneration images including a positive image in which a predeterminedidentification object is viewed and a negative image in which noidentification object is viewed for use in learning to generate theidentifier, extracting feature-point feature-quantities representingfeatures of the feature points of the generation image, generating atotal feature quantity of the generation image from the feature-pointfeature-quantities of the generation image, and generating theidentifier using the total feature quantity of the generation image anda true label indicating whether or not the generation image is apositive image or a negative image, wherein the extracting is performedby dividing a feature-point area into a plurality of small areas byseparating the feature-point area as an area having a feature point as acenter in an angular direction and a distance direction on the basis ofthe feature point in each of a plurality of response images obtained byfiltering the generation image by the plurality of filters, producing astatistical quantity of pixel values of a small area for each of theplurality of small areas, and setting the statistical quantity of eachof the plurality of small areas obtained from each of the plurality ofresponse images for the feature point as a feature-pointfeature-quantity of the feature point, and wherein the generating isperformed by generating the identifier for performing identificationusing a dimensional feature quantity to decrease an error valuerepresenting an identification error level of the positive and negativeimages among a plurality of dimensional feature quantities which arecomponents of the multi-dimensional vector as the total featurequantity, and generating dimensional information representing adimension of the dimensional feature quantity to decrease the errorvalue.
 13. A program for making a computer function as: feature-pointextracting means for extracting feature points as characteristic pointsfrom a processing object image of an object used to identify whether ornot a subject viewed in the image is a predetermined identificationobject; feature-point feature-quantity extracting means for extractingfeature-point feature-quantities representing features of the featurepoints; dimensional-feature-quantity generating means for generating adimensional feature quantity of a dimension represented by dimensionalinformation among a plurality of dimensional feature quantities whichare components of a vector as a total feature quantity represented by amulti-dimensional vector from the feature-point feature-quantities ofthe processing object image, wherein the total feature quantityrepresents a feature of the entire processing object image; andidentification means for identifying whether or not the subject viewedin the processing object image is the predetermined identificationobject by inputting the dimensional feature quantity to an identifierfor identifying whether or not the subject viewed in the processingobject image is the predetermined identification object, wherein thefeature-point feature-quantity extracting means divides a feature-pointarea into a plurality of small areas by separating the feature-pointarea as an area having a feature point as a center in an angulardirection and a distance direction on the basis of the feature point ineach of a plurality of response images obtained by filtering theprocessing object image by a plurality of filters having differentcharacteristics, produces a statistical quantity of pixel values of asmall area for each of the plurality of small areas, and sets thestatistical quantity of each of the plurality of small areas obtainedfrom each of the plurality of response images of the processing objectimage for the feature point as a feature-point feature-quantity of thefeature point, wherein the identifier and dimensional information areobtained by extracting feature points from one of a plurality ofgeneration images including a positive image in which a predeterminedidentification object is viewed and a negative image in which noidentification object is viewed for use in learning to generate theidentifier, extracting feature-point feature-quantities representingfeatures of the feature points of the generation image, generating atotal feature quantity of the generation image from the feature-pointfeature-quantities of the generation image, and generating theidentifier using the total feature quantity of the generation image anda true label indicating whether or not the generation image is apositive image or a negative image, wherein the extracting is performedby dividing a feature-point area into a plurality of small areas byseparating the feature-point area as an area having a feature point as acenter in an angular direction and a distance direction on the basis ofthe feature point in each of a plurality of response images obtained byfiltering the generation image by the plurality of filters, producing astatistical quantity of pixel values of a small area for each of theplurality of small areas, and setting the statistical quantity of eachof the plurality of small areas obtained from each of the plurality ofresponse images for the feature point as a feature-pointfeature-quantity of the feature point, and wherein the generating isperformed by generating the identifier for performing identificationusing a dimensional feature quantity to decrease an error valuerepresenting an identification error level of the positive and negativeimages among a plurality of dimensional feature quantities which arecomponents of the multi-dimensional vector as the total featurequantity, and generating dimensional information representing adimension of the dimensional feature quantity to decrease the errorvalue.
 14. A learning device comprising: a feature-point extractingsection extracting feature points as characteristic points from one of aplurality of generation images including a positive image in which apredetermined identification object is viewed and a negative image inwhich no identification object is viewed for use in learning to generatean identifier for identifying whether or not a subject viewed in animage is the identification object; a feature-point feature-quantityextracting section extracting feature-point feature-quantitiesrepresenting features of the feature points of the generation image; atotal-feature-quantity generating section generating a total featurequantity represented by a multi-dimensional vector from thefeature-point feature-quantities of the generation image, wherein thetotal feature quantity represents a feature of the entire generationimage; and an identifier generating section generating the identifierusing the total feature quantity of the generation image and a truelabel indicating whether or not the generation image is a positive imageor a negative image; wherein the feature-point feature-quantityextracting section divides a feature-point area into a plurality ofsmall areas by separating the feature-point area as an area having afeature point as a center in an angular direction and a distancedirection on the basis of the feature point in each of a plurality ofresponse images obtained by filtering the generation image by aplurality of filters having different characteristics, produces astatistical quantity of pixel values of a small area for each of theplurality of small areas, and sets the statistical quantity of each ofthe plurality of small areas obtained from each of the plurality ofresponse images for the feature point as a feature-pointfeature-quantity of the feature point, and wherein the identifiergenerating section generates the identifier for performingidentification using a dimensional feature quantity to decrease an errorvalue representing an identification error level of the positive andnegative images among a plurality of dimensional feature quantitieswhich are components of the multi-dimensional vector as the totalfeature quantity, and generates dimensional information representing adimension of the dimensional feature quantity to decrease the errorvalue.
 15. An identification device comprising: a feature-pointextracting section extracting feature points as characteristic pointsfrom a processing object image of an object used to identify whether ornot a subject viewed in the image is a predetermined identificationobject; a feature-point feature-quantity extracting section extractingfeature-point feature-quantities representing features of the featurepoints; a dimensional-feature-quantity generating section generating adimensional feature quantity of a dimension represented by dimensionalinformation among a plurality of dimensional feature quantities whichare components of a vector as a total feature quantity represented by amulti-dimensional vector from the feature-point feature-quantities ofthe processing object image, wherein the total feature quantityrepresents a feature of the entire processing object image; and anidentification section identifying whether or not the subject viewed inthe processing object image is the predetermined identification objectby inputting the dimensional feature quantity to an identifier foridentifying whether or not the subject viewed in the processing objectimage is the predetermined identification object, wherein thefeature-point feature-quantity extracting section divides afeature-point area into a plurality of small areas by separating thefeature-point area as an area having a feature point as a center in anangular direction and a distance direction on the basis of the featurepoint in each of a plurality of response images obtained by filteringthe processing object image by a plurality of filters having differentcharacteristics, produces a statistical quantity of pixel values of asmall area for each of the plurality of small areas, and sets thestatistical quantity of each of the plurality of small areas obtainedfrom each of the plurality of response images of the processing objectimage for the feature point as a feature-point feature-quantity of thefeature point, wherein the identifier and dimensional information areobtained by extracting feature points from one of a plurality ofgeneration images including a positive image in which a predeterminedidentification object is viewed and a negative image in which noidentification object is viewed for use in learning to generate theidentifier, extracting feature-point feature-quantities representingfeatures of the feature points of the generation image, generating atotal feature quantity of the generation image from the feature-pointfeature-quantities of the generation image, and generating theidentifier using the total feature quantity of the generation image anda true label indicating whether or not the generation image is apositive image or a negative image, wherein the extracting is performedby dividing a feature-point area into a plurality of small areas byseparating the feature-point area as an area having a feature point as acenter in an angular direction and a distance direction on the basis ofthe feature point in each of a plurality of response images obtained byfiltering the generation image by the plurality of filters, producing astatistical quantity of pixel values of a small area for each of theplurality of small areas, and setting the statistical quantity of eachof the plurality of small areas obtained from each of the plurality ofresponse images for the feature point as a feature-pointfeature-quantity of the feature point, and wherein the generating isperformed by generating the identifier for performing identificationusing a dimensional feature quantity to decrease an error valuerepresenting an identification error level of the positive and negativeimages among a plurality of dimensional feature quantities which arecomponents of the multi-dimensional vector as the total featurequantity, and generating dimensional information representing adimension of the dimensional feature quantity to decrease the errorvalue.